  Accurate estimation of daily rainfall return levels associated with large
return periods is needed for a number of hydrological planning purposes,
including protective infrastructure, dams, and retention basins. This is
especially relevant at small spatial scales. The ERA-5 reanalysis product
provides seasonal daily precipitation over Europe on a 0.25 x 0.25 grid (about
27 x 27 km). This translates more than 20,000 land grid points and leads to
models with a large number of parameters when estimating return levels. To
bypass this abundance of parameters, we build on the regional frequency
analysis (RFA), a well-known strategy in statistical hydrology. This approach
consists in identifying homogeneous regions, by gathering locations with
similar distributions of extremes up to a normalizing factor and developing
sparse regional models. In particular, we propose a step-by-step blueprint that
leverages a recently developed and fast clustering algorithm to infer return
level estimates over large spatial domains. This enables us to produce maps of
return level estimates of ERA-5 reanalysis daily precipitation over continental
Europe for various return periods and seasons. We discuss limitations and
practical challenges and also provide a git hub repository. We show that a
relatively parsimonious model with only a spatially varying scale parameter can
compete well against statistical models of higher complexity.
