Comparing spatial metrics of extreme precipitation between data from rain gauges, weather radar and high-resolution climate model re-analyses

[Display omitted] •Point and radar observations are compared to high-resolution climate model data.•The seasonal distribution of extremes is similar between the datasets.•The intensity levels are similar except for the most coarse climate model data.•There are clear differences in the spatial correlation structure of the extremes.•The spatial correlation metric is robust between very different datasets. The representation of extreme precipitation at small spatio-temporal scales is of major importance in urban hydrology. The present study compares point and radar observations to reanalyse climate model output data for a period of 14 years where there is full spatial and temporal overlap between datasets. The datasets are compared with respect to seasonality of occurrence, intensity levels and spatial structure of the extreme events. All datasets have similar seasonal distributions and comparable intensity levels. There are, however, clear differences in the spatial correlation structure of the extremes. Seemingly, the radar data is the best representation of a “real” spatial structure for extreme precipitation, even though challenges appear in data when moving far from the physical radar. The spatial correlation in point observations is a valid representation of the spatial structure of extreme precipitation. The convective-permitting climate model seems to represent the spatial structure of extreme precipitation much more realistically, compared to the coarser convective parameterized model. However, there is still room for improvement of the convective-permitting climate model for the shortest rainfall durations and smallest spatial scales in comparison with point and radar data.