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\begin{document}

\title{Reconstruction of zonal precipitation from sparse historical
observations using climate model information and statistical learning}



\author[1]{Marius Egli}%
\author[1]{Sebastian Sippel}%
\author[2]{Angeline Greene Pendergrass}%
\author[1]{Iris de Vries}%
\author[1]{Reto Knutti}%
\affil[1]{ETH Zurich}%
\affil[2]{National Center for Atmospheric Research (UCAR)}%


\vspace{-1em}



  
  \date{June 05, 2024}


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\begin{abstract}
Future projected changes in precipitation substantially impact societies
worldwide. However, large uncertainties remain due to sparse historical
observational coverage, large internal climate variability, and climate
model disagreement.\\
Here, we present a novel reconstruction of large-scale zonal
precipitation metrics from sparse rain-gauge data using regularized
regression techniques that are trained across climate model
simulations.\\
Subsequently, we test the reconstruction on independent satellite data
and reanalyzed precipitation, and find a large fraction of historical
zonal mean precipitation variability is recovered, in particular over
the Northern hemisphere and in parts of the tropics. Finally, we
demonstrate that the reconstructed zonal mean precipitation trends are
outside the variability of pre-industrial control simulations, and are
consistent with the range of historical simulations driven by external
forcing. Overall, we illustrate a novel way of estimating
seasonally-averaged zonal precipitation from gauge data, and trends
therein that show a signal very likely caused by human influence.%
\end{abstract}%



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\textbf{Hosted file}

\verb`si_reconstructing_zmp.docx`  available at \url{https://authorea.com/users/535235/articles/598696-reconstruction-of-zonal-precipitation-from-sparse-historical-observations-using-climate-model-information-and-statistical-learning}

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