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\begin{document}

\title{The influence of natural variability on extreme~ monsoons in
Pakistan~~~~~~~~~~~~~}



\author[1]{Moetasim Ashfaq}%
\author[2]{Nathaniel Johnson}%
\author[3]{Fred Kucharski}%
\author[4]{Noah S. Diffenbaugh}%
\author[3]{Muhammad Adnan Abid}%
\author[5]{Matthew F. Horan}%
\author[6]{Deepti Singh}%
\author[1]{Salil Mahajan}%
\author[7]{Subimal Ghosh}%
\author[8]{Auroop R. Ganguly}%
\author[1]{Katherine J. Evans}%
\author[9]{Shafiqul Islam}%
\affil[1]{Computational Sciences and Engineering Division (CSED), Oak Ridge National Laboratory, Oak Ridge, TN, USA}%
\affil[2]{National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA}%
\affil[3]{Earth System Physics, Abdus Salam International Centre for Theoretical Physics, Trieste, Italy}%
\affil[4]{Doerr School of Sustainability, Stanford University, Stanford, CA, USA}%
\affil[5]{Bredesen Center, University of Tennessee, Knoxville, TN, USA}%
\affil[6]{School of the Environment, Washington State University, Vancouver, WA, USA}%
\affil[7]{Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India}%
\affil[8]{Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, Boston, MA, USA}%
\affil[9]{Department of Civil and Environmental Engineering, The Fletcher School of Law and Diplomacy, Tufts University, Medford, MA, USA.}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
\textbf{The monsoons in Pakistan have been exceptionally harsh in recent
decades, resulting in extraordinary drought conditions and record
flooding events. The changing frequency of extreme events is widely
attributed to climate change. However, given this region's long history
of floods and droughts, the role of natural climate variability cannot
be rejected without a careful diagnosis. Here, we examine how oceanic
and atmospheric variability has contributed to unusual precipitation
distributions in West South Asia. Variations in sea surface temperatures
in the tropical Pacific and northern Arabian Sea, and internal
atmospheric variability related to the circumglobal teleconnection
pattern and the subtropical westerly jet stream, explain more than 70\%
of monthly summer precipitation variability in the 21st century. Several
of these forcings have co-occurred with record strength during episodes
of extreme monsoons, which have exacerbated the overall effect. Climate
change may have contributed to increased variability and the in-phase
co-occurrences of the identified mechanisms, but further research is
required to confirm any such connection.}%
\end{abstract}%



\sloppy


\section*{}

{\label{874460}}

\section*{Introduction\(\)}

{\label{874460}}

Pakistan has experienced frequent extreme monsoon events in recent
decades. At the turn of the century, Pakistan was exposed to one of the
most severe droughts in its history (\emph{1, 2}), while the floods of
2010 and 2022 were among the most devastating in modern times (\emph{3,
4}). Considering West South Asia (WSA; 65\selectlanguage{ngerman}°--75E° and 24°--37N°), the
number of months with standardized precipitation anomalies exceeding ± 1
has increased by a factor of \textasciitilde{}1.4 between 1979--2000 and
2001--2022, while the number of months exceeding ± 1.5 has increased by
a factor of 3.5 (Fig. 1). The large variations in the summer monsoon
over WSA make a compelling case for climate change. However, despite the
apparent increase in monsoon extremes, floods and droughts in this
region predate modern climate change (\emph{5, 6}). Therefore, it is
essential to investigate these recent changes in the historical context
to delineate the role of natural climate variability in driving such
anomalies. ~~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/Figure-01-noc/Figure-01nr}
\caption{\selectlanguage{ngerman}{\textbf{July-August-September detrended CPC precipitation anomalies over
West South Asia (WSA; 65--75°E; 24--37°N) during 1979--2022}. Monthly
standardized area-averaged precipitation anomaly (bars) from the average
over 1991--2020. The years when the region experienced floods or
droughts are labeled. The dotted lines represent standardized Niño1+2
(blue), Niño3 (yellow), Niño3.4 (grey), and Niño4 (orange) indices using
detrended COBE/NOAA sea surface temperatures. For ease of comparison,
all Niño indexes are multiplied by -1 so that positive (negative)
anomalies represent La Niña (El Niño). The number in parentheses
represents the actual correlation between WSA and Niño indexes.
{\label{307505}}%
}}
\end{center}
\end{figure}\selectlanguage{ngerman}

Although a substantially stronger (weaker) South Asian monsoon can
significantly enhance (reduce) summer precipitation over WSA, the
remarkably wet conditions experienced in the northwestern and southern
Pakistan regions in recent years are extraordinarily unusual. These
areas lie at the boundary of the South Asian monsoon and are usually
beyond the reach of the most energetic weather systems capable of
producing significant precipitation. The dry entrainment in the lower
atmosphere, the strong westerlies in the upper atmosphere over WSA (Fig.
2), and the low heat budget in the adjacent Arabian Sea make conditions
unfavorable for rain-producing weather systems (\emph{7}). Several
factors -- such as atmospheric wave activity triggered by distant
teleconnections, circumglobal wave patterns, or jet stream anomalies --
can overcome this unfavorable background environment and provide
conditions conducive to above-normal monsoons (\emph{8-11}). However, it
is yet to be determined how much of a role these factors have played in
recent extremes. ~

Many of Pakistan's extreme monsoons have happened during years with
anomalous sea surface temperatures (SSTs) in the equatorial Pacific in
connection with the phenomenon known as the El Niño--Southern
Oscillation (ENSO). The most notable are droughts in 1997, 2002, and
2004 during El Niño (positive ENSO) and floods in 2010, 2011, and 2022
during La Niña (negative ENSO) (Fig. 1). Nevertheless, the monthly
teleconnection of ENSO forcing on WSA summer precipitation is weak: not
only is the severity of extreme monsoon conditions in Pakistan not
proportional to the strength of ENSO forcing, but in several cases, the
negative teleconnection even breaks downs. For instance, drought
persisted in Pakistan even with extreme La Niña conditions during
1998--1999; likewise, despite strong El Niño, seasonal monsoon
precipitation was average in 2015. This implies that other contributing
factors, such as co-occurrences of other modes of variability,
compensate for or compound the impact of ENSO.

~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-S01nr/Figure-S01nr}
\caption{{\textbf{The atmospheric circulation patterns during 1979--2022}. The
upper (200 hPa) and lower (850 hPa) jet streams during the WSA summer
monsoon season (July-August-September). The vectors (colors) represent
the wind direction (speed). The rectangles indicate WSA (white) and
areas used for two land surface temperature indexes. White contours
identify South Asian high and jet stream maxima.
{\label{521987}}%
}}
\end{center}
\end{figure}

Evidence suggests that jet stream meandering in the Northern Hemisphere
may be one of the contributors (\emph{10-13}). On average, the
subtropical westerly jet lies north of South Asia during the monsoon
season (Fig. 2). In the summer of 2010, persistent extratropical
upper-level blocking over western Russia led to the penetration of a
deep trough over northern Pakistan through jet stream meandering
(\emph{10}). Western Russia suffered a record heatwave, and Pakistan
experienced an unprecedented flooding event as a result. Likewise, in
August 2022, the jet stream anomalies led to an upper-level atmospheric
high over East Asia, created a frontal boundary over Pakistan, and
pushed the South Asian monsoon trough south of its normal position.
These conditions guided low-pressure monsoon systems westward and then
meridionally through Pakistan along the frontal boundary (See animation
in \emph{Animation\_1.mov}). The outcome was a record heatwave over East
Asia (\emph{14}) and catastrophic flooding over Pakistan. Apart from
anomalies in the subtropical westerly jet, the Circumglobal
Teleconnection (CGT) pattern -- the second leading mode of the upper
atmosphere circulations in the Northern Hemisphere -- has also been
known to influence the precipitation distribution over WSA during summer
(\emph{15}), but its role in causing recent extreme monsoons is
unclear.~

\par\null

\textbf{Hosted file}

\verb`Animation_1.mov`  available at \url{https://authorea.com/users/532254/articles/630317-the-influence-of-natural-variability-on-extreme-monsoons-in-pakistan}

\subsubsection*{}

{\label{805175}}

\emph{\textbf{Animation Details}: The snapshot of atmospheric
circulation (ERA5) and precipitation (CPC) over South Asia and the
surrounding areas every three hours from August 10 to 26, 2022}

\emph{}

While ENSO forcing, jet stream meandering, and circumglobal wave
activity are not new phenomena, the monsoon over Pakistan has become
more erratic in recent years (Fig. 1). The recent extreme monsoon events
have raised several questions. Is the increase in extreme monsoons over
Pakistan due to natural variability alone, or has anthropogenic global
warming played a role? If the former is true, has ENSO's influence on
WSA precipitation increased in recent decades, or are other local or
remote factors at play? Could these extremes result from the compounding
effect caused by multiple forcings occurring simultaneously (e.g.,
remote teleconnections, jet stream anomalies, increased atmospheric
water vapor, etc.)?

This study aims to investigate and answer these questions directly or
indirectly. We hypothesize that internal atmospheric variability, such
as anomalies in the jet stream and/or circumglobal wave activity, has
contributed to unusual precipitation distributions over the WSA
independent of well-known large-scale natural forcings. We examine this
hypothesis using a regression model incorporating independent tropical
and extratropical forcings capable of producing dynamical anomalies
associated with extreme monsoons over the WSA. ~

\section*{Results and Discussion}

{\label{319227}}

Due to its location on the western edge of the South Asian monsoon,
summer (July-September) precipitation in WSA is highly variable
month-to-month, with a monthly standard deviation exceeding 70\% of the
seasonal average over most areas. The highest amounts of summer
precipitation are experienced over Kashmir and northern Punjab in
Pakistan (Fig. 3A). The strength of the monsoon is greatly diminished by
the time it reaches eastern Afghanistan through the western border of
Pakistan. However, monsoon magnitudes and variability have increased
significantly in the peripheral regions of WSA over the past few
decades. Notably, the mean and variability of precipitation along the
Pakistan-Afghanistan border and Sindh province in southern Pakistan
increased by over 100\% between 1979--2000 and 2001--2022 (Fig. 3). The
apparent increase in monsoon rains over these regions suggests a spatial
shift in the location of wet anomalies maxima. An analysis of wet years
with flooding since 1979 shows that in recent decades there have been
more instances when excess precipitation has fallen in the northwestern
and southern areas of WSA that historically received only trace amounts
of monsoon precipitation (Fig. 4). ~\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-02nr/Figure-02nr}
\caption{{\textbf{July-August-September precipitation characteristics over WSA and
its changes between 1979--2000 and 2001--2022}. Average (\textbf{A})
precipitation, (\textbf{B}) standard deviation in 1979--2022. The
difference between average (\textbf{C}) precipitation and (\textbf{D})
standard deviation in 1979--2000 and 2001--2022. The stippling
represents the statistical significance of differences at the 95\%
confidence level.
{\label{423753}}%
}}
\end{center}
\end{figure}

\par\null

\textbf{Identification of drivers}

To understand the factors influencing changing precipitation patterns
over WSA, it is imperative to determine what conditions allow monsoon
weather systems to travel over peripheral regions where heavy monsoon
rains do not normally penetrate. In addition to tropical forcings, our
hypothesis that jet stream anomalies and/or circumglobal wave patterns
can independently affect the monsoon distribution over WSA requires that
we look beyond oceanic teleconnections. Jet stream anomalies and
circumglobal wave activity often significantly affect land surface
temperatures (\emph{16-18}). Therefore, in addition to examining the
relationship of WSA precipitation variability with natural variations in
sea surface temperatures, we also examine connections between WSA
precipitation variability and surface temperature anomalies over land.
This will enable comprehensive identification of the modes of internal
atmospheric variability that regulate anomalies in WSA precipitation.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-S02nr/Figure-S02nr}
\caption{{\textbf{Precipitation anomalies during Pakistan's flood years}.~Average
precipitation (top left) during the reference period (1991--2020).
Precipitation departures from 1991--2020 average when Pakistan
experienced floods. The months representing each event are provided in
each panel.~
{\label{300781}}%
}}
\end{center}
\end{figure}

An analysis of the spatial correlation map of detrended WSA
precipitation and global surface temperatures reveals several oceanic
and terrestrial regions of possible association. This includes SSTs in
the tropical Pacific, north-central Pacific, and northern Arabian Sea,
and land surface temperatures in East Asia, north-central Asia, Arabia,
northeast Africa, and western Russia (Fig. 5A). These correlations may
not be independent because large-scale climate forcings such as ENSO
simultaneously affect many terrestrial regions through eastward
propagating Rossby waves (\emph{19}). Variability associated with ENSO
in the tropical Pacific can also influence other oceanic basins through
inter-basin interactions (\emph{20}). As a result, many of these
significant correlations between WSA precipitation and temperature over
land and ocean regions may no longer exist once the influence of ENSO
has been removed.

Therefore, we adopt a sequential procedure to construct a multi-linear
regression model explaining WSA precipitation variability in which each
additional explanatory variable is selected after eliminating the
influence of the preceding explanatory variable from precipitation and
surface temperatures. This process is repeated until all regions of
interest have been identified. (See~\emph{Methods for complete
details.}) Based on this approach, three oceanic and two land regions
are identified as potential independent contributors to precipitation
variability over WSA, including the east-central Pacific (i.e., the
Ni\selectlanguage{ngerman}ño3.4 region), West Pacific (WP), North Arabian Sea (NAS), East and
north-central Asia, and Arabia/northeast Africa (Fig. 5B-5F).~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/Figure-03-nr/Figure-03-nr}
\caption{\selectlanguage{ngerman}{\textbf{Identification of relevant forcings affecting precipitation
variability over WSA}. (\textbf{A}) Pearson correlation between monthly
(July-August-September) WSA precipitation index (cyan rectangle box) and
global surface temperatures. Same as (A), but after linearly removing
the influence of (\textbf{B}) Niño3.4 temperature index from A,
(\textbf{C}) West Pacific temperature index from B, (\textbf{D}) East
and north-central Asia temperature index from C, (\textbf{E})
Arabia/northeast Africa temperature index from D. (\textbf{F})
represents the final correlation map after removing the influence of all
five forcings. The red rectangle boxes and labels in (A) to (E)
represent areas used to define each index. The black contours represent
the statistical significance of correlations at the 90\% confidence
level.his is a caption
{\label{345011}}%
}}
\end{center}
\end{figure}\selectlanguage{ngerman}

Given that the last two explanatory variables based on land surface
temperatures (Fig. 3D, 3E) lack an obvious physical justification, we
perform additional analyses to identify their robust connection with
internal atmospheric variability. For this purpose, we calculate
Empirical Orthogonal Functions (EOFs) using monthly 200 hPa geopotential
height anomalies over an area encompassing 20°E--130°E and 20°N--65°N
after linearly removing the influence of Niño3.4 and the WP SST
variability. The subtropical westerly jet stream variability within this
region is most relevant for the South Asian climate during the summer
months. The resulting EOF patterns 1 through 6 are sufficiently
separated from each other and explain \textgreater{}75\% of the total
variance (Fig. 6). However, we limit our focus to the first two EOFs
that explain 21\% and 17\% of the total variance, respectively, and are
the most related to the land-based temperature indexes and WSA
precipitation variability. ~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-S03nr/Figure-S03nr}
\caption{{\textbf{Most common modes of the upper-atmosphere geopotential height
variability}. The first six modes of EOF analyses of
July-August-September 200 hPa geopotential height anomalies. The numbers
in the parenthesis above each panel represent the explained variance.
Each EOF pattern has been multiplied by the standard deviation of its
principal component. All six EOF patterns are sufficiently separated
based on the North's rule of thumb.
{\label{971395}}%
}}
\end{center}
\end{figure}

The spatial structure of the first EOF resembles the CGT pattern
identified by Ding and Wang (\emph{15}). It is possible to investigate
this correspondence by regressing geopotential height anomalies onto the
standardized index of CGT (see~\emph{Methods}) and the Principal
Component (PC) time series of the first EOF. Regression maps derived
from this analysis show a pattern correlation of 0.91 across the area
used in EOF analyses and 0.69 across the Northern Hemisphere (Fig. 7).
We also find a high temporal correlation (0.59) between the PC time
series and the CGT index (Fig. 8), further supporting their close
relationship. Therefore, we conclude that the first mode of 200 hPa
geopotential height variability represents the CGT pattern.~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-04nr/Figure-04nr}
\caption{{\textbf{Relationship between the leading mode of July-August-September
geopotential height variability and CGT}. The 200 hPa monthly
geopotential height (Z200) anomalies regressed onto the standardized
(\textbf{A}) Principal Component associated with the first EOF (PC1) of
Z200 and (\textbf{B}) CGT index. The black dotted box represents the
area used to perform EOF analyses. The green contours represent
statistically significant areas at the 95\% confidence level. The
analyses cover 1979--2022.
{\label{387009}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-S04nr/Figure-S04nr}
\caption{{\textbf{Relationship between indexes}. CGT versus the principal
component corresponding to the first EOF (PC1) of 200 hPa geopotential
height variability.
{\label{909957}}%
}}
\end{center}
\end{figure}

Ding and Wang (\emph{15}) also showed that the CGT pattern negatively
correlates with temperature distributions in the Arabian Peninsula and
the northeastern parts of Africa, which we have used to define one of
our temperature indexes (Fig. 5D). In our analysis, this assertion holds
only for the 21st century period when the temperature index over Arabia
and northeast Africa significantly correlates with the CGT index (the
correlation is -0.16 in 1979--2022 and -0.46 in 2001--2022; Fig. 9A). To
discern the increasing influence of CGT on the pattern of temperature
anomalies over Arabia and northeast Africa, we regress geopotential
height anomalies onto the standardized temperature index (rectangle box
shown in Fig. 5D) separately for the 1979--2000 and 2001--2022 periods
(Fig. 10). When compared with the corresponding regression map based on
the CGT index, the regression map for 2001--2022 (1979--2000) shows a
pattern correlation of 0.91 (0.44) across the area used in EOF analyses
and 0.63 (0.2) across the Northern Hemisphere. These associations
indicate that this temperature index is closely related to the CGT
pattern in recent decades. Therefore, we refer to it as the
temperature-based CGT index (TCGT).~\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-S05nr/Figure-S05nr}
\caption{{\textbf{Relationship between indexes}. (\textbf{A}) CGT versus TCGT,
(\textbf{B}) the principal component corresponding to the second EOF
(PC2) of 200 hPa geopotential height variability versus TSWJ. For ease
of comparison, the TCGT time series is multiplied by -1. The numbers on
each panel represent actual correlations during 2001--2022 (1979--2000).
{\label{240622}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-05nr/Figure-05nr}
\caption{{\textbf{Relationship between CGT and TCGT}. The 200 hPa monthly
geopotential height (Z200) anomalies are regressed onto the standardized
(\textbf{A}, \textbf{B}) CGT and (\textbf{C}, \textbf{D}) TCGT indexes.
The TCGT is multiplied by -1 for ease of comparison. The black dotted
box represents the area used to perform EOF analyses. The green contours
represent statistically significant areas at the 95\% confidence level.
The analyses cover 1979--2000 (left column) and 2001--2022 (right
column).
{\label{132252}}%
}}
\end{center}
\end{figure}

We note that the geopotential-based CGT index (\emph{15}) significantly
correlates with WSA precipitation, but the CGT correlation is not as
strong as that of TCGT. Over the 1979--2022 period, the correlation of
the CGT index with the WSA precipitation index is 0.27, while that of
TCGT is -0.39. These significant correlations increase to 0.37 and -0.51
in recent decades (2001--2022). These comparisons suggest that the CGT
pattern most affects the WSA precipitation distribution when it aligns
in a way that affects the temperature distribution in the areas used to
determine the TCGT.~~

The temperature dipole over East and north-central Asia (rectangle boxes
shown in Fig. 5C) strongly relates to the second EOF of 200 hPa
geopotential height (Fig. 6). We regress geopotential height anomalies
onto the standardized Principal Component (PC) time series of the second
EOF and this temperature index (Fig. 11). Comparing their regression
maps reveals a pattern correlation of 0.95 across the area used in EOF
analyses and 0.85 across the Northern Hemisphere. A robust temporal
correlation (0.68) also exists between the corresponding PC time series
of the second EOF and the temperature index (Fig. 5C) for the entire
analysis period (1979--2022) (Fig. 9B), which remains stable between the
early (1979--2000; 0.7) and recent (2001--2022; 0.66) periods. These
strong spatial and temporal correlations suggest that the temperature
dipole over East and north-central Asia can be a proxy for the
subtropical westerly jet variability (second EOF). Therefore, we call it
the temperature-based subtropical westerly jet index (TSWJ). As in the
case of the CGT index, the PC time series related to the second EOF
correlates less strongly with the WSA precipitation index than the TSWJ
index. Over the 1979--2022 period, the PC time series correlation with
the WSA precipitation index is 0.27, while that of TSWJ is 0.41. This
implies that when this mode of jet stream variability is aligned to have
a maximum impact on temperature distribution in the two regions that
define the TSWJ index, precipitation in WSA is most impacted by the
meandering of the subtropical westerly jet stream.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-06nr/Figure-06nr}
\caption{{\textbf{Relationship between the second leading mode of
July-August-September geopotential height variability and TSWJ}. The 200
hPa monthly geopotential height (Z200) anomalies regressed onto the
standardized (\textbf{A}) TSWJ index and (\textbf{B}) Principal
Component associated with the second EOF (PC2) of Z200. The black dotted
box represents the area used to perform EOF analyses. The green contours
represent statistically significant areas at the 95\% confidence level.
The analyses cover 1979--2022.
{\label{578846}}%
}}
\end{center}
\end{figure}

\textbf{The influence of drivers}

Knowing that the precipitation characteristics of WSA have changed
significantly between the early (1979--2000) and recent (2001--2022)
periods, we perform separate regression analyses for the two periods
(after standardizing the data over the full 1979--2022 period). The
regression model reveals a marked difference in explanatory power
between the early and recent periods, as the coefficient of
determination (R-squared) more than doubles across the entire region
(Fig. 12A). Its maximum value remains below 0.35 for the early period,
while it reaches 0.7 in many areas of Pakistan's southern half for the
recent period.~

The increase in explained variance over time has resulted from stronger
correlations between WSA precipitation and all five independent
forcings. For ease of comparison, the impact of each independent
variable is expressed as the standardized precipitation anomaly
(dependent variable) in response to per unit change in its standardized
value (Fig. 12B-12F). The most notable shift in influence comes from the
WP; while it provides little additional explanation of late-20th-century
precipitation variability, it has been the most significant and
widespread driver since 2000. The next major shift in influence is from
TCGT, which displays a substantial increase in effect over the southern
and eastern WSA. Other forcings also show an increase in their impact on
precipitation variability. ENSO (Ni\selectlanguage{ngerman}ño 3.4) has a weak negative
teleconnection over the east-northeast WSA in the early period, which
becomes strong and spatially shifts westward over northwestern Pakistan
-- an area that has seen more frequent heavy precipitation events in
recent years. The positive impact of warm SST anomalies in the northern
Arabian Sea is predominantly felt in the southern WSA, which similarly
gains strength, particularly along the coast. Likewise, the influence of
TSWJ, represented by the East/north-central Asia temperature index,
increases in strength and spatial footprint in the later period,
substantially increasing over Balochistan and adjacent Afghanistan
areas. Outside the WSA, these indexes also exhibit significant influence
along the Western Ghats of southern India and central and northeastern
South Asia.~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-07nr/Figure-07nr}
\caption{\selectlanguage{ngerman}{\textbf{Multiple linear regression analyses of monthly WSA
precipitation. (A) The coefficient of determination (R2)}. The
standardized regression coefficients describe each explanatory
variable's influence on WSA precipitation, including (\textbf{B})
Niño3.4, (\textbf{C}) WP, (\textbf{D}) TSWJ, (\textbf{E}) TCGT, and
(\textbf{F}) NAS. The first and third columns represent 1979--2000, and
the second and fourth columns represent 2001--2022. The purple contours
show statistically significant areas at 95\% confidence levels. The
numbers in each panel represent the correlation between the time series
of each explanatory variable and the WSA precipitation index.
(\textbf{G}) The actual and regressed monthly (July-August-September)
anomalies from 2001 through 2022.
{\label{248438}}%
}}
\end{center}
\end{figure}\selectlanguage{ngerman}

Given the increased precipitation variability, a high number of wet and
dry extremes, and the substantial explanatory power of the regression
model in the recent period, we mostly focus on the 21st-century period
in the rest of the analyses. The regression based on standardized data
over the length of the recent period displays high skill in calculating
monthly precipitation variability (R\textsuperscript{2}= 0.74). There
has only been one notable exception in the sign of the precipitation
anomaly since 2000, when the model failed to capture below-normal
monsoon rains in 2009 (Fig. 12G), highlighting the potential role of
other factors not considered in the regression. The explanatory power of
the regression model for the recent period does not substantially change
(R\textsuperscript{2}= 0.71) when regression is performed over the
entire period (Fig. 13).

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/Figure-S06nr/Figure-S06nr}
\caption{{\textbf{Multiple linear regression analyses of WSA precipitation over
1979--2000}. The actual and regressed monthly (July-August-September)
anomalies during 1979 and 2022.
{\label{548902}}%
}}
\end{center}
\end{figure}

To understand how each of the five forcings influences the variability
of WSA precipitation in the recent period, we repeat multiple linear
regression analyses using geopotential height as the dependent variable
instead of precipitation for the 2001--2022 period. A separate analysis
is conducted for geopotential height fields at 850 and 200 hPa. An
individual regression coefficient represents geopotential anomalies in
geopotential meters per unit change in the standardized explanatory
variable (Fig. 14). However, before analyzing the dynamic anomalies
caused by these forcing factors, it is imperative to understand why the
vigor of rain-producing systems decreases as they approach the
peripheral monsoon regions of the WSA. Once we know the climatological
atmospheric conditions over the WSA, it should be easier to comprehend
how atmospheric anomalies caused by individual forcings shape associated
anomalies in precipitation distributions.

In the lower atmosphere over the Arabian Sea, before the northern flank
of the Somali jet comes onshore, its moist flow mixes with the dry air
entraining from the northwest. A narrow passageway formed by the
Hindukush mountains to the east and the Lut desert to the west channels
this dry air from central Asia (Fig. 2). The dry entrainment keeps
southwest parts of South Asia under conditions less favorable for moist
convection (\emph{21}). As a result, the monsoon trough forms along the
Himalayas between the Bay of Bengal and northwestern South Asia rather
than having an east-west orientation. These areas also sit on the
western edge of the South Asian High in the upper atmosphere as the
southern flank of the subtropical westerly jet runs diagonally through
the WSA (Fig. 2). Consequently, low-pressure monsoon systems moving
along the monsoon trough cannot penetrate further west (\emph{22}).
Other factors, such as interaction with the topography and the depletion
of moisture supply far inland, also favor the dissipation of these
systems by the time they reach Kashmir and Punjab in Pakistan. As a
result, southern, southwestern, and western South Asia remain relatively
dry.

The climatological conditions over WSA (Fig. 2) highlight several
prerequisites that must be met before the monsoon can penetrate the
southern and western parts of the region. These factors include reduced
dry entrainment from the northwest into the Somali jet over the Arabian
Sea and weakened onshore flow over southern WSA, allowing the
possibility of an east-west orientation of the monsoon trough.
Additionally, a more favorable placement of the subtropical westerly jet
should allow monsoon rain-producing systems to penetrate the
west-northwest regions of WSA. With this knowledge of the preconditions,
we now analyze the regression coefficients associated with the forcings
used in the regression model.~

The coefficients corresponding to Ni\selectlanguage{ngerman}ño3.4 represent atmospheric
anomalies induced by La Niña. They show an anomalous easterly flow from
the Bay of Bengal through central India to southern parts of WSA (Fig.
14A). This reflects a reduction in dry air entrainment over the northern
Arabian Sea and southern WSA. In the upper atmosphere, an anomalous high
to the northeast and a low to the northwest create a frontal boundary
over northwestern Pakistan that should help facilitate the movement of
low-pressure systems through that area. The tropical easterly jet also
shows strengthening over the western Indian Ocean and the Arabian Sea.
It also supports an anomalous barotropic high over western Russia,
which, combined with the low to the northwest of Pakistan, partly
resembles atmospheric anomalies during July-August 2010. Anomalously low
Niño3.4 values during this period indicate the occurrence of an
unusually strong La Niña for this time of year. ~

Lower-level circulation anomalies induced by Niño3.4 and WP forcings are
peculiarly similar from East Asia to the southwestern boundary of WSA
through the Bay of Bengal and central India (Fig. 14B). However, the WP
influence differs from Niño3.4 in other areas. In addition to reducing
dry air entrainment over WSA, the WP weakens the Somali jet over the
Arabian Sea and southwesterly flow over the Bay of Bengal by inducing
anomalous westward flow from the western Pacific/eastern Indian Ocean.
The resulting changes in monsoon circulation weaken the monsoon over
Bangladesh and northeast India and redistribute moist flow over western
South Asia. In the upper atmosphere, the WP forcing places a
quasi-diagonally elongated low-pressure anomaly between northern Egypt
and to the west of northwest Pakistan and a high-pressure anomaly over
East Europe. These conditions represent a weakening of westerly flow
over the northern WSA that should allow monsoon lows to penetrate
further west. Furthermore, the placement of the anomalous low along the
western Pakistan border guides the movement of monsoon lows meridionally
through Pakistan. The corresponding precipitation regression maps show
that these dynamical anomalies bring excess monsoon rains throughout the
region. The warming in the northern Arabian Sea leads to an anomalous
cyclonic circulation over the Arabian Sea, with the center off the
coasts of the states of Gujarat and Maharashtra in western India (Fig.
14C). The excess moisture convergence, as a result, increases rainfall,
particularly along the coastal belt in these Indian states and Sindh in
Pakistan, as well as upstream regions in central Pakistan.~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/Figure-08nr/Figure-08nr}
\caption{\selectlanguage{ngerman}{\textbf{Multiple linear regression analyses of atmospheric circulation
anomalies}. The regression coefficients describe atmospheric anomaly
patterns (850 hPa and 200 hPa) associated with each standardized
explanatory variable in the regression model. (\textbf{A}) Niño3.4,
(\textbf{B}) WP, (\textbf{C}) NAS (\textbf{D}) TSWJ, (\textbf{E}) TCGT.
The vectors represent atmospheric winds, and the colors represent
geopotential height. The thick and thin black contours represent the
statistical significance of geopotential height regression coefficients
at 95\% and 90\% confidence levels, respectively. The analyses cover
1979--2022. For ease of comparison, we multiply coefficients
corresponding to Niño34, WP, and TCGT by -1 so that all coefficients
reflect those phases of forcings that induce positive precipitation
anomalies over WSA.
{\label{542438}}%
}}
\end{center}
\end{figure}\selectlanguage{ngerman}

As previously shown, TCGT represents the CGT pattern, and TSWJ is a
proxy for the second EOF of 200 hPa geopotential height variability. In
response to TSWJ forcing, the subtropical westerly jet is forced
northward over East Asia, which creates an anomalous barotropic shear
over South Asia and places the monsoon trough south of its original
position over southern WSA (Fig. 14D). TCGT also causes a similar
increase in barotropic shear in the lower atmosphere and a southward
movement of the monsoon trough over South Asia (Fig. 14E). Anomalies
with such characteristics facilitate the westward trajectory of Bay of
Bengal monsoon lows towards southern Pakistan and produce conditions
conducive to the genesis of monsoon vortices (See animation in
\emph{Animation\_2.mov}). TSWJ induces much stronger dry entrainment
from eastern Asia over northeastern South Asia, and consequently, the
negative precipitation response is greater than that of TCGT. In
contrast, the moisture convergence along the monsoon trough, caused by
increased westerly flow from the northern Indian Ocean, is stronger
under TCGT than under TSWJ, which significantly influences precipitation
over Central India. Both forcings lead to a high-pressure anomaly in
northwest WSA, reflecting a northwest shift in the westerlies in the
upper atmosphere. Such conditions facilitate the movement of
rain-producing weather systems over the southern-southwestern WSA, but
keep them out of the northwestern WSA. As a result, these forcings have
a limited role in causing excess precipitation over the northwestern WSA
in wet months.

\par\null

\textbf{Hosted file}

\verb`Animation_2.mov`  available at \url{https://authorea.com/users/532254/articles/630317-the-influence-of-natural-variability-on-extreme-monsoons-in-pakistan}

\subsubsection*{}

{\label{571990}}

\textbf{\emph{Animation Details}}\emph{: Isosurface of specific humidity
(0.0123 Kg/Kg) colored by zonal winds. The strongest colors represent
magnitudes greater than ± 10 m/sec. Warm (cold) colors indicate eastward
(westward) flow. The animation runs from July 01 to August 31, 2022. The
convergence zone generally represents the position of the monsoon
trough. The data used in this animation is obtained from ERA5
reanalysis. The animation shows a snapshot every three hours.}

\par\null

\textbf{Robustness of identified forcings}

A critical question is whether combining these five forcings can
accurately reproduce atmospheric anomalies associated with precipitation
variability over South Asia and its surrounding regions. The answer to
this question should allow us to determine whether these forcings are
robust drivers of precipitation variability over WSA in recent decades.
We seek answers to this question in two ways. First, we perform a
composite analysis of precipitation distribution and overlying
circulation patterns during wet and dry months over WSA. Even though the
regression model only explains the precipitation variance over WSA, we
assume the WSA anomalies cannot be completely disassociated from the
average large-scale pattern of precipitation anomalies across South
Asia. Therefore, we compare precipitation composites over the entire
South Asia in this analysis. There have been stronger wet extremes than
dry extremes over WSA during the last 22 years. Using a 1.0 standard
deviation threshold, we identify eight extreme wet months and four
extreme dry months; using a 0.75 standard deviation threshold increases
the number of wet and dry months to 11 and 13, respectively. Note that
the area-averaged monthly precipitation distribution in WSA is highly
skewed and does not contain \textasciitilde{}32\% of the total events
within one standard deviation (which would be expected in a normal
distribution). Therefore, we use the 0.75 standard deviation threshold
for composite analyses. Second, we analyze individual monthly cases to
understand the dynamical patterns associated with WSA precipitation
extremes and their reproducibility through the regression model. In this
case, we consider those 12 months where below-normal or above-normal WSA
precipitation departures exceed one standard deviation. The
precipitation anomalies in these individual cases are only shown over
WSA.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.56\columnwidth]{figures/Figure-09nr/Figure-09nr}
\caption{{\textbf{Composite analyses of atmospheric circulation and precipitation
anomalies in 2001--2022}. The composites of wet (\textbf{A}, \textbf{B})
and dry (\textbf{C}, \textbf{D}) precipitation anomalies in South Asia
and associated atmospheric (850 and 200 hPa) anomalies. The left column
represents actual precipitation (circulation) anomalies in CPC (ERA5),
and the right column represents corresponding anomalies in the regressed
data. The vectors represent atmospheric winds, and the colors represent
geopotential height. The wet and dry anomalies are defined using a 0.75
standard deviation threshold of the WSA precipitation index.
{\label{234557}}%
}}
\end{center}
\end{figure}

Anomalously wet months feature a southward-shifted monsoon trough, a
cyclonic anomaly over the northern Arabian Sea, and a substantially
weakened entrainment of dry northerly flow over the Arabian Sea and
southern WSA in the lower atmosphere (Fig. 15A). The dipolar anomaly in
the north and east of South Asia in the upper atmosphere, resembling the
TSWJ-induced pattern, highlights the importance of subtropical westerly
jet stream meandering in shaping conditions conducive to
wetter-than-normal monsoon rains over WSA. Additionally, an upper
atmosphere high, partly resembling the TCGT-induced pattern, is needed
over northwest WSA to displace westerlies so that monsoon lows can
penetrate further west over South Asia (Fig. 15B). The anomalies over
higher latitudes and East Asia are barotropic in nature. The regressed
patterns represent all these key features of atmospheric anomalies
supporting wet conditions over WSA. Interestingly, the correspondence of
broad precipitation anomalies between actual and regressed patterns is
not limited to WSA. The suppression over Bangladesh and northeastern
India, and enhancement over Myanmar, central India, and the Western
Ghats, compare remarkably well between the actual and regressed
patterns, indicating that these forcing mechanisms explain precipitation
anomalies across South Asia.

The magnitude of precipitation suppression during drier-than-normal
months is not comparable to wet extremes during wetter-than-normal
months over WSA (Fig. 15C). This asymmetry is to be expected, as the dry
anomalies cannot exceed the climatological precipitation amounts a
region receives. Therefore, the strongest dry anomalies would always
occur in areas that, on average, receive the highest rainfall during the
monsoon season. As a result, while the maximum wet anomalies occur over
the peripheral monsoon regions of WSA, the maximum dry anomalies occur
over the eastern parts of WSA that climatologically experience more
monsoon rains. Apart from this artifact, the broad pattern of anomalies
in precipitation distribution during below-average months is the
opposite of that observed in above-average months, with less rain
falling in the Western Ghats and central India and excessive rain
falling in northern India.

While atmospheric anomalies during the dry months composite are not an
exact reversal of those during the wet months composite, some features
are comparable. The anticyclonic anomaly over the Arabian Sea, the
reversal of jet stream-related anomalies over East Asia and Siberia, and
the northward shift in the monsoon trough are a few features that are
opposite to the anomalies during wet months (Fig. 15C, 15D).
Additionally, the westerly jet moves further south over western Asia,
preventing rain-producing weather systems from having trajectories
through WSA. A substantial weakening of tropical easterlies over the
Indian Ocean is also present during dry extremes. The strength of the
tropical easterly jet stream is normally associated with the strength of
the South Asian summer monsoon (\emph{23}). Therefore, their weakening
reflects that the South Asian monsoon is likely weaker-than-normal when
below-normal monsoon rains are received over WSA. Stronger-than-normal
tropical easterly jet activity is not clear in the wet months composite.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-S07nr/Figure-S07nr}
\caption{{\textbf{Analyses of atmospheric circulation and precipitation anomalies
in extreme months}. The wet and dry precipitation anomalies over WSA and
associated atmospheric anomalies at 850hPa. The first and third columns
represent actual precipitation (circulation) anomalies in CPC (ERA5),
and the second and fourth columns represent corresponding anomalies in
the regressed data. The vectors represent atmospheric winds, and the
colors represent geopotential height. The wet and dry anomalies are
defined using a 1.0 standard deviation threshold of the WSA
precipitation index. The analyses cover 2001--2022.
{\label{784942}}%
}}
\end{center}
\end{figure}

The regression-based anomalies are mostly comparable to the actual
patterns in those months when precipitation amounts exceed one standard
deviation, with the most notable differences being in August 2018 and
2021 (Figs. 16 and 17). The two most common features across all these
extreme months are lower atmosphere cyclonic (anticyclonic) anomalies
over the Arabian Sea and southward (northward) shifts in the monsoon
trough in wet (dry) months. The wet anomalies over the southern WSA most
commonly require anomalous high pressure over the northwestern WSA and a
jet stream pattern normally induced by positive TSWJ forcing over East
Asia in the upper atmosphere. The wet anomalies over northwestern WSA
mostly require an overlying trough in the upper atmosphere.
Nevertheless, the dynamical anomalies in dry or wet months display a
substantial variety in patterns causing those extremes, highlighting the
complexity of interactions that shape the distribution of monsoon
extremes over WSA.~~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-S08nr/Figure-S08nr}
\caption{{Same as in Figure 16 but with circulation anomalies at 200 hPa.
{\label{262536}}%
}}
\end{center}
\end{figure}

\textbf{Co-occurring non-ENSO factors}

According to our analysis, internal atmospheric variability and other
oceanic forcings have contributed to unusual precipitation distributions
over the WSA independent of conventionally defined ENSO (i.e., by the
Ni\selectlanguage{ngerman}ño3.4 index). In addition, we can provide answers to the questions
that motivated this study. It is not only the negative ENSO
teleconnection that has strengthened and shifted spatially in the 21st
century, contributing to excessive rainfall in Pakistan's northwest and
neighboring areas in Afghanistan; two other oceanic forcings also have a
role in precipitation variability over the WSA. A dipole of SSTs between
the equatorial western Pacific and northeast of the Australian coast (WP
index; rectangle boxes in Fig. 5B) provides additional Pacific Ocean
influence on WSA precipitation variability independent of ENSO. There is
a need for additional empirical and model-based research to understand
the true nature of WP forcing. Nevertheless, it does not appear to be a
remnant of SST variability in the equatorial central Pacific. The
Northern Hemisphere atmospheric wave response to WP forcing differs
substantially from the response to Niño3.4 (Fig. 18). A second oceanic
forcing can be attributed to anomalies in the adjacent Arabian Sea (NAS
index; rectangle box in Fig. 5E). Although conditions in the northern
Arabian Sea primarily determine the NAS forcing, it generally represents
variability in SSTs across the tropics, independent of Niño3.4 and WP
(Fig. 5E, 5F). Also, the CGT pattern and the meandering of the
subtropical westerly jet have played an instrumental role in
facilitating the dynamic forces necessary to guide monsoon lows over the
peripheral regions of the WSA.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/Figure-S09nr/Figure-S09nr}
\caption{\selectlanguage{ngerman}{Comparison of atmospheric wave response in the Northern Hemisphere
produced by Niño34 and WP forcings. The regression coefficients describe
atmospheric anomaly patterns associated with standardized Niño3.4 (left)
and WP (right) forcings. The colors represent geopotential height
anomalies. The thick and thin green contours represent the statistical
significance of regression coefficients at 95\% and 90\% confidence
levels, respectively. The analyses cover 2001--2022.
{\label{372488}}%
}}
\end{center}
\end{figure}\selectlanguage{ngerman}

Moreover, there is evidence that more synchronous occurrences of these
forcings have shaped the unusual and unprecedented distribution of
monsoon rains over WSA over the past few decades. This conclusion can be
demonstrated by a simple transformation of the monthly time series
representing five forcings. First, we multiply the standardized indexes
associated with Niño3.4, WP, and TCGT with -1 so that positive values in
each index should positively correlate with WSA precipitation
variability and vice versa. Second, we assign a -1 (+1) value to monthly
indexes crossing -1 (+1) and replace the rest of the time series with
zeros. Lastly, we sum each month across the five indexes. The resulting
time series displays an increase in the simultaneous occurrence of those
phases of the five forcings that compound each other's impact. For
example, during 1979--2000, there were ten instances when net in-phase
forcings were two or more, compared with sixteen cases during
2001--2022. Furthermore, from 1979 to 2022, six times the net in-phase
forcings were three or more, with all but one occurring between 2001 and
2022 (Fig. 19).

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Figure-10nr/Figure-10nr}
\caption{\selectlanguage{ngerman}{\textbf{Net complimentary forcings}. The sum of all forcings after their
standardized monthly indexes are transformed into +1/-1 or 0 using ±1
standard deviation as a threshold. The resulting +ve (-ve) number
represents the net forcings at the monthly scale that positively
(negatively) influence WSA precipitation. Niño3.4, WP, and NAS forcings
are multiplied by -1, so positive values in each index positively
correlate with WSA precipitation.
{\label{970751}}%
}}
\end{center}
\end{figure}\selectlanguage{ngerman}

There has also been a significant change in the variability of some of
these forcings in the 21st century. With a 30\% increase in variability,
WP exhibits the greatest change in the recent period. The standardized
WP forcings were strongest in July 2022 (-4.0), August 2022 (-3.3), and
August 2010 (2.9) (Fig. S20). These were the three months during which
Pakistan experienced flooding. The standardized TSWJ in August 2022
(3.0) and TCGT in July 2022 (-2.4) were also the highest and second
strongest during 1979-2022. TSWJ exhibits a 10\% increase in
variability, while TCGT displays a slight decrease of -5\%. The other
forcings, Niño3.4 and NAS, show reduced variability in 2001--2022, with
18\% and 15\% declines compared to 1979--2000. How these decreases might
have impacted their influences on WSA precipitation is unclear.~

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-S10nr/Figure-S10nr}
\caption{\selectlanguage{ngerman}{The standardized indexes of WSA precipitation and five forcings. The
shaded brown/green months represent precipitation anomalies over WSA
exceeding ±1 standard deviation.
{\label{788131}}%
}}
\end{center}
\end{figure}\selectlanguage{ngerman}

\textbf{Implications and outlook}

Our study shows that natural variability in oceanic and atmospheric
processes is responsible for the increased variability in precipitation
over the WSA in recent decades. The monsoon over South Asia is one of
the most extensively researched topics in climate science. However,
monsoon precipitation variability over WSA is relatively poorly
understood. Since most of the WSA lies at the periphery of the South
Asian monsoon, heavy rains rarely penetrate the region, and
precipitation variability (relative to precipitation mean) is
significantly higher than in other parts of South Asia. Thus, it has
unique prerequisites for extreme monsoons that involve the simultaneous
occurrence of multiple naturally occurring modes of oceanic and
atmospheric variability, creating an environment where weather systems
can move over the western periphery of the South Asian monsoon. We have
shown that the number of naturally occurring WSA-relevant modes in these
synchronous occurrences has increased in recent decades and that several
of these forcings have occurred with record strength. These factors have
led to substantially more variability in precipitation and unprecedented
rains over WSA in the 21st century.

Despite our ability to replicate atmospheric patterns and anomalies in
precipitation distribution associated with wet and dry extremes over WSA
through multiple linear regression, several knowledge gaps remain that
will require further investigation. For example, a deeper understanding
of SST dipole-based WP forcing is required. Moreover, while we have
clarified why we preferred land-based temperature indexes as explanatory
variables over PC time series and CGT index, additional research is
necessary to identify more robust indexes that could serve as predictors
representing relevant modes of atmospheric variability that influence
WSA precipitation.

Additionally, we do not reject the possibility of climate change
contributing to increased precipitation variability in the WSA. Among
the consequences of climate change are record-high temperatures on land,
such as those recorded in Russia in 2010 and East Asia in 2022. Although
the atmospheric patterns that have caused these particular heatwaves
have been attributed to natural oceanic and atmospheric variability
(\emph{24, 25}), climate change may have a role in intensifying these
dynamic anomalies. There is also evidence that climate change has caused
more compound extremes over the past few decades (\emph{26, 27}). The
increased in-phase co-occurrence of factors affecting WSA precipitation
variability may also be related to climate change, though that
connection has not yet been established. Moreover, we do not exclude the
possibility of thermodynamic influences, such as increased atmospheric
water vapor, that can enhance precipitation intensity when these
in-phase co-occurrences occur. We have analyzed linear associations
between understudied sources of natural variability and WSA
precipitation variability, probably proximate forcings of extremes in a
changing climate, but it cannot be ruled out that nonlinear forces could
play a role, which must be investigated separately.

Taken together, our results clearly identify modes of variability as a
critical influence on recent extreme events in the region and also
highlight the need for further research to systematically and robustly
identify a clear link between climate change and extreme monsoons in the
WSA.

\par\null

\section*{Materials and Methods}

{\label{162699}}

\textbf{Datasets}

The analyses presented in this study are based on global land
precipitation, land and ocean surface temperatures, atmospheric
geopotential height, meridional winds, zonal winds, and specific
humidity. From 1979 to 2022, it covers the months of July, August, and
September, except for September 2022. Global land precipitation and
maximum and minimum temperatures are sourced from the Climate Prediction
Center (CPC), with a horizontal grid spacing of 0.5° by 0.5°. The mean
land temperature is calculated by averaging the maximum and minimum
temperatures. Global sea surface temperature (SST) data come from two
sources. The Centennial in situ Observation-Based Estimates (COBE;
(\emph{28})) SST, available at 1.0° by 1.0°, covers 1979 to 1981, and
the NOAA high-resolution analysis of SST, available at 0.25° by 0.25°,
covers 1982 to 2022. The National Oceanic and Atmospheric Administration
(NOAA) Physical Science Laboratory, Boulder, Colorado, USA
(https://psl.noaa.gov) provides all global land precipitation and land
and ocean surface temperature datasets. The remaining atmospheric
variables are retrieved from the European Centre for Medium-Range
Weather Forecasts Reanalysis 5 (ERA5), available at 0.25° x 0.25°
horizontal grid spacing (\emph{29}). We combine multiple SST sources to
ensure the highest resolution data is used for the investigation. The
SSTs and atmospheric variables are remapped to the CPC data grid (0.5°
by 0.5°) using the bilinear interpolation function implemented in the
Climate Data Operators (CDO; \url{https://code.mpimet.mpg.de}).

\textbf{Methods}

We focus on the West South Asia (WSA) region from 65°--75E° and
24°--37N°, which encompasses most of Pakistan, western parts of India,
and eastern parts of Afghanistan. In South Asia, the monsoon season
lasts from June to September. However, the WSA region receives monsoon
precipitation between July and September; all monsoon-related extremes
over this region have occurred during these months. Therefore, all
analyses are based on this July-to-September monsoon season.

We remove the monthly least square linear trend at each grid point from
all datasets for analyses. Anomalies are calculated using 1991--2010 as
the reference period. To develop a regression model for WSA monsoon
precipitation, we identify possible explanatory variables based on the
global land and ocean temperature correlations with the WSA
precipitation index. Our method involves selecting surface
temperature-based independent explanatory variables sequentially, where
each previous explanatory variable's influence is removed linearly
before the next is identified. We start with a Pearson correlation map
of the WSA precipitation index and global surface temperatures over
1979--2000. Many oceanic and land regions show significant association
with WSA precipitation, including SSTs in the equatorial Pacific, North
Pacific, and northern Arabian Sea, and land surface temperatures in East
Asia, north-central Asia, Arabia, northeast Africa, and western Russia
(Fig. 5A).

As ENSO is the most robust natural forcing, we begin by selecting the
Niño3.4 index (shortened to Niño3.4 in the text), which is the
area-averaged SST anomaly from 190--240°E and -5--5°N, as our first
explanatory variable. Niño3.4 SSTs display a negative association with
WSA precipitation, consistent with the general understanding that ENSO
exhibits a negative teleconnection with the South Asian summer monsoon.
Removing the Niño3.4 influence from precipitation and temperature
eliminates tropical Pacific correlation except for an area in the
western Pacific exhibiting a meridional correlation dipole (Fig. 5B). We
select this dipole as the second explanatory variable for the regression
model, defined as the temperature difference in the rectangle area
spanning 147°E--162°E and 5°S--5°N from that spanning 147°E--162°E and
15°S--10°S. WSA precipitation's correlation with western Russia
temperatures diminishes once the tropical Pacific influence is removed,
but several other correlations remain intact, including a significant
positive correlation with surface temperatures over East Asia and a
significant negative correlation with surface temperatures over
north-central Asia bordering Mongolia and Siberia. Due to their
location, we assume these temperature anomalies are likely related and
reflect the meandering of the subtropical westerly jet stream (Fig. 2).
Therefore, the third explanatory variable is based on the temperature
difference between the area covering 102--112°E and 28--33°N and the
area covering 80--105°E and 48--53°N (Fig. 5C). The fourth explanatory
variable in our regression model is the temperature over Arabia and
northeast Africa. This area negatively correlates with the CGT pattern
(\emph{15, 30}) (Fig. 9) and therefore represents the influence of one
of the dominant modes of internal atmospheric variability in the
Northern Hemisphere. We defined it as the temperature over the region
covering 28--50°E and 15--24°N (Fig 5D). The fifth and final explanatory
variable uses SSTs in the North Arabian Sea between 60--68°E and
20--26°N, which have a strong positive relationship with WSA
precipitation variability (Fig 5E). The importance of each of the five
selected explanatory variables is determined by comparing the
R\textsuperscript{2}~and F-test statistics (Fig. 21).

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-S11nr/Figure-S11nr}
\caption{{\textbf{The importance of each explanatory variable in multiple linear
regression analyses of monthly (July-August-September) precipitation}.
Precipitation variability explained by the (multi; row 2--5) linear
regression model, expressed as the coefficient of determination (R2),
with each explanatory variable added. Stippling represents the 95\%
confidence level for F-statistics significance. The analyses are shown
for the full (1979--2022; left column), early (1979--2000; middle
column), and recent (2001--2022; right column) periods. The red
rectangular box represents West South Asia.
{\label{817598}}%
}}
\end{center}
\end{figure}

After removing the influence of these indexes, no major areas of
significant correlation exist between WSA precipitation and global
surface temperature except for an area in the northern Pacific (Fig 5F).
Using this region as an additional explanatory variable in the
regression does not add much to the explained variance, and therefore
this region is not considered for analyses. The rectangle boxes for the
explanatory variable 2 to 5 represent those areas that yield maximum
correlation with the WSA precipitation index after area averaging. All
area averages are performed after weighting the data with the cosine of
latitudes.~~

We determined a statistically significant difference in precipitation
mean and variability between early (1979--2000) and recent (2001--2022)
periods across WSA (Fig. 2) through Student's t-test and F-test,
respectively. Therefore, we use the five explanatory variables to
investigate the drivers of these precipitation differences by performing
separate multiple regression analyses for the early and recent periods.
Similar analyses are conducted for the entire period (1979--2022).
Additionally, we perform multiple regression analyses separately for
geopotential height, zonal winds, and meridional winds as dependent
variables.

All regression analyses that compare early and recent periods or cover
the entire length of the analysis period employ standardized data based
on 1979--2022. The regression analyses focused on the recent
21st-century period use standardized data covering only that period. All
regression results are tested for statistical significance using
analysis of variance.

To further aid the analyses, the CGT index is defined as geopotential
height area weighted averaged over 35\selectlanguage{ngerman}°--40°N, 60°--70°E, following Ding
and Wang (\emph{15}). Similarly, Niño4, Niño3, and Niño1+2 indexes are
defined using the climate data guide provided by the National Center for
Atmospheric Research
(\url{https://climatedataguide.ucar.edu/climate-data}).

\section*{}

{\label{596485}}

\section*{References}

{\label{335824}}

1.~~~~~~~~ H. Xie, C. Ringler, T. J. Zhu, A. Waqas, Droughts in
Pakistan: a spatiotemporal variability analysis using the Standardized
Precipitation Index. \emph{Water Int} \textbf{38}, 620-631 (2013).

2.~~~~~~~~ S. Adnan, K. Ullah, S. T. Gao, Investigations into
Precipitation and Drought Climatologies in South Central Asia with
Special Focus on Pakistan over the Period 1951-2010. \emph{J Climate}
\textbf{29}, 6019-6035 (2016).

3.~~~~~~~~ S. Mallapaty, S. Soofi, Z. Memon, Pakistan's floods have
displaced 32 million people - here's how researchers are helping.
\emph{Nature} \textbf{609}, 667-667 (2022).

4.~~~~~~~~ K. Gaurav, R. Sinha, P. K. Panda, The Indus flood of 2010 in
Pakistan: a perspective analysis using remote sensing data. \emph{Nat
Hazards} \textbf{59}, 1815-1826 (2011).

5.~~~~~~~~ B. Weil, The Rivers Come: Colonial Flood Control and
Knowledge Systems in the Indus Basin, 1840s--1930s. \emph{Environment
and History} \textbf{12}, 27 (2006).

6.~~~~~~~~ S. Dutt, A. K. Gupta, B. Wunnemann, D. D. Yan, A long arid
interlude in the Indian summer monsoon during similar to 4,350 to 3, 450
cal. yr BP contemporaneous to displacement of the Indus valley
civilization. \emph{Quatern Int} \textbf{482}, 83-92 (2018).

7.~~~~~~~~ S. S. C. Shenoi, D. Shankar, S. R. Shetye, Differences in
heat budgets of the near-surface Arabian Sea and Bay of Bengal:
Implications for the summer monsoon. \emph{J Geophys Res-Oceans}
\textbf{107},~ (2002).

8.~~~~~~~~ S. Saeed, W. A. Muller, S. Hagemann, D. Jacob, Circumglobal
wave train and the summer monsoon over northwestern India and Pakistan:
the explicit role of the surface heat low. \emph{Clim Dynam}
\textbf{37}, 1045-1060 (2011).

9.~~~~~~~~ F. S. Syed, J. H. Yoo, H. Kornich, F. Kucharski,
Extratropical influences on the inter-annual variability of South-Asian
monsoon. \emph{Clim Dynam} \textbf{38}, 1661-1674 (2012).

10.~~~~~~ W. K. M. Lau, K. M. Kim, The 2010 Pakistan Flood and Russian
Heat Wave: Teleconnection of Hydrometeorological Extremes. \emph{J
Hydrometeorol} \textbf{13}, 392-403 (2012).

11.~~~~~~ G. Di Capua \emph{et al.}, Drivers behind the summer 2010 wave
train leading to Russian heatwave and Pakistan flooding. \emph{Npj Clim
Atmos Sci} \textbf{4},~ (2021).

12.~~~~~~ A. Bibi, K. Ullah, Y. S. Zhou, Z. Q. Wang, G. T. Shouting,
Role of Westerly Jet in Torrential Rainfall During Monsoon Over Northern
Pakistan. \emph{Earth Space Sci} \textbf{7},~ (2020).

13.~~~~~~ A. Schneidereit \emph{et al.}, Large-Scale Flow and the
Long-Lasting Blocking High over Russia: Summer 2010. \emph{Mon Weather
Rev} \textbf{140}, 2967-2981 (2012).

14.~~~~~~ Z. Q. Wang, H. L. Luo, S. Yang, Different mechanisms for the
extremely hot central-eastern China in July-August 2022 from a Eurasian
large-scale circulation perspective. \emph{Environ Res Lett}
\textbf{18},~ (2023).

15.~~~~~~ Q. H. Ding, B. Wang, Circumglobal teleconnection in the
Northern Hemisphere summer. \emph{J Climate} \textbf{18}, 3483-3505
(2005).

16.~~~~~~ X. L. Chen, T. J. Zhou, Relative contributions of external SST
forcing and internal atmospheric variability to July-August heat waves
over the Yangtze River valley. \emph{Clim Dynam} \textbf{51}, 4403-4419
(2018).

17.~~~~~~ J. Q. Yang \emph{et al.}, Atmospheric Circumglobal
Teleconnection Triggered by Spring Land Thermal Anomalies over West Asia
and Its Possible Impacts on Early Summer Climate over Northern China.
\emph{J Climate} \textbf{34}, 5999-6021 (2021).

18.~~~~~~ W. W. Wang, W. Zhou, X. Wang, S. K. Fong, K. C. Leong, Summer
high temperature extremes in Southeast China associated with the East
Asian jet stream and circumglobal teleconnection. \emph{J Geophys
Res-Atmos} \textbf{118}, 8306-8319 (2013).

19.~~~~~~ K. E. Trenberth \emph{et al.}, Progress during TOGA in
understanding and modeling global teleconnections associated with
tropical sea surface temperatures. \emph{J Geophys Res-Oceans}
\textbf{103}, 14291-14324 (1998).

20.~~~~~~ W. J. Cai \emph{et al.}, Pantropical climate interactions.
\emph{Science} \textbf{363}, 944-+ (2019).

21.~~~~~~ W. R. Boos, J. V. Hurley, Thermodynamic Bias in the Multimodel
Mean Boreal Summer Monsoon. \emph{J Climate} \textbf{26}, 2279-2287
(2013).

22.~~~~~~ D. Rastogi \emph{et al.}, Characteristics of Bay of Bengal
Monsoon Depressions in the 21st Century. \emph{Geophys Res Lett}
\textbf{45}, 6637-6645 (2018).

23.~~~~~~ D. R. Pattanaik, V. Satyan, Fluctuations of Tropical Easterly
Jet during contrasting monsoons over India: A GCM study. \emph{Meteorol
Atmos Phys} \textbf{75}, 51-60 (2000).

24.~~~~~~ R. Dole \emph{et al.}, Was there a basis for anticipating the
2010 Russian heat wave? \emph{Geophys Res Lett} \textbf{38},~ (2011).

25.~~~~~~ D. Q. Zhang, L. J. Chen, Y. Yuan, J. Q. Zuo, Z. J. Ke, Why was
the heat wave in the Yangtze River valley abnormally intensified in late
summer 2022? \emph{Environ Res Lett} \textbf{18},~ (2023).

26.~~~~~~ M. R. Alizadeh \emph{et al.}, A century of observations
reveals increasing likelihood of continental-scale compound dry-hot
extremes. \emph{Sci Adv} \textbf{6},~ (2020).

27.~~~~~~ J. Wang \emph{et al.}, Anthropogenically-driven increases in
the risks of summertime compound hot extremes. \emph{Nat Commun}
\textbf{11},~ (2020).

28.~~~~~~ S. Hirahara, M. Ishii, Y. Fukuda, Centennial-Scale Sea Surface
Temperature Analysis and Its Uncertainty. \emph{J Climate} \textbf{27},
57-75 (2014).

29.~~~~~~ H. Hersbach \emph{et al.}, The ERA5 global reanalysis. \emph{Q
J Roy Meteor Soc} \textbf{146}, 1999-2049 (2020).

30.~~~~~~ M. Almazroui, I. U. Rashid, S. Saeed, M. N. Islam, ENSO
influence on summer temperature over Arabian Peninsula: role of
mid-latitude circulation. \emph{Clim Dynam} \textbf{53}, 5047-5062
(2019).

\section*{}

{\label{596485}}

\section*{Acknowledgments}

{\label{687807}}

This research used the Oak Ridge Leadership Computing Facility
resources, a DOE Office of Science User Facility supported under
Contract DE-AC05-00OR22725.

\textbf{Funding:}

This work is supported by the U.S. Air Force Numerical Weather Modeling
Program and National Climate\selectlanguage{english}-Computing Research Center, located within
the National Center for Computational Sciences at the ORNL, and
supported under a Strategic Partnership Project 2316\selectlanguage{english}-T849\selectlanguage{english}-08 between DOE
and NOAA.

\textbf{Author contributions:}

M.A. designed the study and performed the analyses. All authors were
involved in the discussion of results. M.A. wrote the manuscript draft
and finalized it with feedback from all authors.

\textbf{Competing interests:}

The authors declare that they have no competing interests.

\textbf{Data and materials availability:}

All datasets used in this analysis are publicly available. The
Centennial in situ Observation-Based Estimates SST, the NOAA
high-resolution analysis of SST, and the CPC precipitation and
temperature data are provided by the NOAA PSL, Boulder, Colorado, USA,
from their website at \url{https://psl.noaa.gov}. The ERA5 reanalysis is
available from the Copernicus Climate Change Service (C3S) Climate Date
Store (\url{https://cds.climate.copernicus.eu/}).

\selectlanguage{english}
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