  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
