Evaluation of Daily Precipitation Extremes in Reanalysis and Gridded Observation‐Based Data Sets Over Germany
Guannan Hu, Christian L. E. Franzke
Abstract
Abstract Accurate and reliable gridded data sets are important for analyzing extreme weather and climate events. Specifically, these data sets should produce extreme value statistics that are close to reality. Here we use various statistical methods to evaluate the quality of four gridded data products in representing daily precipitation extremes. The data products are the COSMO‐REA6 regional reanalysis, the ERA5 global reanalysis, and the E‐OBS and HYRAS gridded observation‐based data sets. The statistical methods we use offer a thorough insight into the quality of the different data sets by providing temporal and spatial extreme value statistics of daily precipitation. Our results show that all data sets except HYRAS underestimate the magnitude of daily precipitation extremes when compared with weather station data. Moreover, the reanalysis data sets give generally worse extreme value statistics of daily precipitation than the gridded observation‐based data sets. In particular, the reanalysis data sets often fail in reproducing the accurate timing of observed daily precipitation extremes.