Abstract Climate change is expected to change precipitation characteristics and particularly the frequency and magnitude of precipitation extremes. Satellite observations form an important part of the observing system necessary to monitor both temporal and spatial patterns of precipitation variability and extremes. As satellite-based precipitation estimates are generally only indirect, however, their reliability has to be verified. This study evaluates the ability of the satellite-based Global Precipitation Climatology Project One-Degree Daily (GPCP1DD) dataset to reliably reproduce precipitation variability and extremes over Europe compared to the European Daily High-resolution Observational Gridded Dataset (E-OBS). The results show that the two datasets agree reasonably well not only when looking at climatological statistics such as climatological mean, number of wet days (rain rates 1 mm), and mean intensity (i.e., mean over all wet days) but also with respect to their distributions. The results also reveal a pronounced seasonal cycle in the performance of GPCP1DD that is worse in winter and spring. Both deterministic and fuzzy verification methods are used to assess the ability of the GPCP1DD dataset to capture extremes. Fuzzy methods prove to be the better suited evaluation approach for such a highly variable parameter as precipitation because it compensates for slight spatial and temporal displacements. Whereas the deterministic diagnostics confirm previous findings on the deficiencies of satellite products, the “fuzzy” results show that at larger spatiotemporal scales (e.g., 3°/5 days) GPCP1DD has useful skill and is able to reliably represent the spatial and temporal variability of extremes.