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High-resolution climatic data are essential to many applications inenvironmental research. Here we develop a new semi-mechanistic downscalingapproach for daily precipitation that incorporates high resolution (30 arc sec)satellite-derived cloud frequency. The downscaling algorithm incorporatesorographic predictors such as wind fields, valley exposition, and boundarylayer height, with a subsequent bias correction. We apply the method to theERA5 precipitation archive and MODIS monthly cloud cover frequency to develop adaily gridded precipitation time series in 1km resolution for the years 2003onward. Comparison of the predictions with existing gridded products andstation data indicates an improvement in the spatio-temporal performance of thedownscaled data in predicting precipitation. Regional scrutiny of the cloudcover correction from a topographically highly heterogeneous area furtherconfirms that CHELSA-EarthEnv performs well in comparison to otherprecipitation products such as numerical weather models. The presentedCHELSA-EarthEnv daily precipitation product improves the temporal accuracycompared to ERA5 with an additional improved in spatial accuracy and muchbetter representation of precipitation in complex terrain

Global daily 1km land surface precipitation based on cloud cover-informed downscaling