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, uneven spatial distributions of rain gauges and trends associated with anthropogenic climate change. Here, 100-year return values of daily precipitation are estimated on a global grid based on a large data set of model-generated precipitation events from ensemble weather prediction. In this way, the statistical uncertainties in the return values can be substantially reduced compared to observational estimates due to the substantially longer time series. In spite of a general agreement in spatial patterns, the model-generated data set leads to systematically higher return values than the observations in many regions, with statistically significant differences, for instance, over the Amazon, western Africa, the Arabian Peninsula and India. This might be linked to an overestimation of tropical extreme precipitation in the model or an underestimation of extreme precipitation events in observations, which, if true, would have important consequences for practical water management.
