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\begin{document}

\title{The Multi-Scale Interactions of Atmospheric Phenomenon in Extreme and
Mean Precipitation}



\author[1]{Andreas Franz Prein}%
\author[2]{Priscilla A. Mooney}%
\author[1]{James Done}%
\affil[1]{National Center for Atmospheric Research (UCAR)}%
\affil[2]{NORCE Norwegian Research Centre}%


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  \date{May 21, 2024}


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\begin{abstract}
Globally, extreme precipitation events cause enormous impacts. Climate
change increases the frequency and intensity of extreme precipitation,
which in combination with rising population enhances exposure to major
floods. An improved understanding of the atmospheric processes that
cause extreme precipitation events would help to advance predictions and
projections of such events. To date, such analyses have typically been
performed rather unsystematically and over limited areas (e.g., the
U.S.) which has resulted in contradictory findings. Here we present the
Multi Object Analysis of Atmospheric Phenomenon (MOAAP) algorithm that
uses a set of nine common atmospheric variables to identify and track
tropical and extra-tropical cyclones, anticyclones, atmospheric rivers
(ARs), mesoscale convective systems (MCSs), and frontal zones. We apply
the algorithm to global historical data between 2000 to 2020. We find
that MCSs produce the vast majority of extreme precipitation in the
tropics and some mid-latitude land regions, while extreme precipitation
in mid- and high-latitude ocean and coastal regions are dominated by
cyclones and ARs. Importantly, most extreme precipitation events are
associated with interacting features across scales that intensify
precipitation. These interactions, however, can be a function of the
rarity (e.g., return period) of extreme events. The presented
methodology and results could have wide-ranging applications including
training of machine learning methods, lagrangian-based evaluation of
climate models, and process-based understanding of extreme precipitation
in a changing climate.%
\end{abstract}%



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