ИСТИНА

In this work, statistical downscaling methods (machine learning) are used to obtain probabilistic characteristics of intense precipitation from low-resolution atmospheric hydrodynamic modeling fields. The maximum daily precipitation in the Moscow region according to long-term observations at weather stations (1988 –2020) is presented as a predictand. The characteristic description variables are physically based large-scale predictors of intense precipitation, calculated using ERA5 reanalysis data and averaged over the Moscow region domain territory. The Ridge Regressor model was taken as a baseline. Compared to the base value of the average amount for the territory, it shows a significant improvement in the reproduction of precipitation characteristics. Rating for the feature importance of large-scale atmospheric predictors for the territory of the Moscow region was also shown.