High-impact river floods are often caused by very extreme precipitation events with return periods of several decades or centuries, and the design of flood protection measures thus relies on reliable estimates of the corresponding return values. However, calculating such return values from observations is associated with large statistical uncertainties due to the limited length of observational time series. Here, estimates of 100-year return values of daily precipitation on a global grid based on a large data set of model-generated precipitation events from ensemble weather prediction are presented, in which statistical uncertainties of the return values are substantially reduced compared to observational estimates.
