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\begin{document}

\title{A Nonparametric Statistical Technique for Spatial Downscaling of
Precipitation over High Mountain Asia}



\author[1]{Yiwen Mei}%
\author[2]{Viviana Maggioni}%
\author[2]{Paul R Houser}%
\author[2]{Yuan Xue}%
\author[2]{Tasnuva Rouf}%
\affil[1]{University of Michigan}%
\affil[2]{George Mason University}%


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  \date{May 15, 2024}


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\begin{abstract}
The accurate representation of the local-scale variability of
precipitation plays an important role in understanding the hydrological
cycle and land-atmosphere interactions in the High Mountain Asia region.
Therefore, the development of hyper-resolution precipitation data is of
urgent need. In this study, we propose a statistical framework to
downscale the Modern-Era Retrospective analysis for Research and
Applications, version 2 (MERRA-2) precipitation product using the random
forest classification and regression algorithm. A set of variables
representing atmospheric, geographic, and vegetation cover information
are selected as model predictors, based on a recursive feature
elimination method. The downscaled precipitation product is validated in
terms of magnitude and variability against a set of ground- and
satellite-based observations. Results suggest improvements with respect
to the original resolution MERRA-2 precipitation product and comparable
performance with gauge-adjusted satellite precipitation products.%
\end{abstract}%



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