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\begin{document}

\title{Towards narrowing uncertainty in future projections of local extreme
precipitation}



\author[1]{Francesco Marra}%
\author[2]{Moshe Armon}%
\author[3]{Ori Adam}%
\author[4]{Davide Zoccatelli}%
\author[5]{Osama Moh'd Najeeb Gazal}%
\author[2]{Chaim I Garfinkel}%
\author[6]{Dorita Rostkier-Edelstein}%
\author[7]{Uri Dayan}%
\author[2]{Yehouda Enzel}%
\author[8]{Efrat Morin}%
\affil[1]{Institute of Atmospheric Sciences and Climate, National Research Council}%
\affil[2]{Hebrew University of Jerusalem}%
\affil[3]{Hebrew University}%
\affil[4]{University of Padova}%
\affil[5]{Faculty of Agricultural and Environmental Sciences, Szent Istvan University}%
\affil[6]{Department of Environmental Physics, Environmental Sciences Division, IIBR}%
\affil[7]{Department of Geography, Hebrew University of Jerusalem}%
\affil[8]{Institute of Earth Sciences, The Hebrew University of Jerusalem}%


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  \date{May 24, 2024}


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\begin{abstract}
Projections of extreme precipitation based on modern climate models
suffer from large uncertainties. Specifically, unresolved physics and
natural variability limit the ability of climate models to provide
actionable information on impacts and risks at the regional, watershed
and city scales relevant for practical applications. Here we show that
the interaction of precipitating systems with local features can
constrain the statistical description of extreme precipitation. These
observational constraints can be used to project local extremes of low
yearly exceedance probability (e.g., 100-year events) using
synoptic-scale information from climate models, which is generally
represented more accurately than the local-scales, and without requiring
climate models to explicitly resolve extremes. The novel approach offers
a path for improving the predictability of local statistics of extremes
in a changing climate, independent of pending improvements in climate
models at regional and local scales.%
\end{abstract}%



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