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Accurate estimation of daily rainfall return levels associated with largereturn periods is needed for a number of hydrological planning purposes,including protective infrastructure, dams, and retention basins. This isespecially relevant at small spatial scales. The ERA-5 reanalysis productprovides seasonal daily precipitation over Europe on a 0.25 x 0.25 grid (about27 x 27 km). This translates more than 20,000 land grid points and leads tomodels with a large number of parameters when estimating return levels. Tobypass this abundance of parameters, we build on the regional frequencyanalysis (RFA), a well-known strategy in statistical hydrology. This approachconsists in identifying homogeneous regions, by gathering locations withsimilar distributions of extremes up to a normalizing factor and developingsparse regional models. In particular, we propose a step-by-step blueprint thatleverages a recently developed and fast clustering algorithm to infer returnlevel estimates over large spatial domains. This enables us to produce maps ofreturn level estimates of ERA-5 reanalysis daily precipitation over continentalEurope for various return periods and seasons. We discuss limitations andpractical challenges and also provide a git hub repository. We show that arelatively parsimonious model with only a spatially varying scale parameter cancompete well against statistical models of higher complexity.

High return level estimates of daily ERA-5 precipitation in Europe estimated using regionalised extreme value distributions