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\begin{document}

\title{Separating weather and climate using a spatially-scalable precipitation
model with optimized subseasonal-to-seasonal statistics}



\author[1]{Daniel J. Short Gianotti}%
\author[2]{Guido D. Salvucci}%
\author[2]{Bruce T. Anderson}%
\affil[1]{Massachusetts Institute of Technology}%
\affil[2]{Boston University}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\begin{abstract}
We present a kernel auto-regressive (KA) method which can be used to
represent the daily to multi-day auto-correlation structure of
precipitation time series, using information both in the occurrence and
intensity of measured rainfall events. The method is able to capture a
larger fraction of the memory in multiple time series than commonly-used
occurrence-based Markov chain models, even when the intensity
distribution is allowed to be conditioned on the Markov state. The KA
method is less sensitive to the spatial scale at which the data is
reported, as it is not strictly reliant on patterns of wet and dry days
for providing correlation. Output from the KA model can be used as
weather generator model simulations, as empirical representations of
process structure, as representation of weather/climate variability
partitioning, or as climatological null models against which
observations can be compared for statistical significance. The KA method
demonstrates improvements in each of these over classic occurrence
Markov chain models and daily independent climatology, in both
representations of interannual precipitation variability and in
downstream water balance variables. We provide climate null confidence
intervals for precipitation trends (driven largely by autumn increases),
and decompose variability into trend, interannual, and weather
components (in increasing order of magnitude) for the Contiguous United
States.%
\end{abstract}%



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