Abstract/Summary

Accurate and reliable gridded datasets are important for analyzing extreme weather and climate events. Specifically, these datasets 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 datasets. The statistical methods we use offer a thorough insight into the quality of the different datasets by providing temporal and spatial extreme value statistics of daily precipitation. Our results show that all datasets except HYRAS underestimate the magnitude of daily precipitation extremes when compared with weather station data. Moreover, the reanalysis datasets give generally worse extreme value statistics of daily precipitation than the gridded observation-based datasets. In particular, the reanalysis datasets often fail in reproducing the accurate timing of observed daily precipitation extremes.