  High-resolution climatic data are essential to many applications in
environmental research. Here we develop a new semi-mechanistic downscaling
approach for daily precipitation that incorporates high resolution (30 arc sec)
satellite-derived cloud frequency. The downscaling algorithm incorporates
orographic predictors such as wind fields, valley exposition, and boundary
layer height, with a subsequent bias correction. We apply the method to the
ERA5 precipitation archive and MODIS monthly cloud cover frequency to develop a
daily gridded precipitation time series in 1km resolution for the years 2003
onward. Comparison of the predictions with existing gridded products and
station data indicates an improvement in the spatio-temporal performance of the
downscaled data in predicting precipitation. Regional scrutiny of the cloud
cover correction from a topographically highly heterogeneous area further
confirms that CHELSA-EarthEnv performs well in comparison to other
precipitation products such as numerical weather models. The presented
CHELSA-EarthEnv daily precipitation product improves the temporal accuracy
compared to ERA5 with an additional improved in spatial accuracy and much
better representation of precipitation in complex terrain

Atmospheric and Oceanic Physics