Dataset Description This dataset contains spatial maps of extreme daily precipitation quantiles over Italy derived from six Remote Sensing and Reanalysis (RSR) products: IMERG, CMORPH, MSWEP, GSMaP, CHIRPS, and ERA5. The analysis covers the period from January 2002 to December 2023. For each dataset, extreme precipitation quantiles (mm/day) were estimated for four return periods (10, 50, 100, and 200 years) using two statistical approaches: Generalized Extreme Value distribution (GEV) applied at the native spatial resolution of each product (Von Mises, 1936). Metastatistical Extreme Value Distribution (MEVD) applied both at the native spatial resolution (Marani and Ignaccolo 2015) and after applying a stochastic downscaling method for extreme-value statistics grounded in random field theory (Zorzetto and Marani 2019). The downscaling approach enables the estimation of extreme rainfall quantiles at point scale, bridging the gap between spatially averaged satellite and reanalysis estimates and point-scale rainfall statistics. Dataset Contents The dataset includes a total of 72 georeferenced raster maps (.tiff format): - 24 GEV maps at native resolution - 24 MEVD maps at native resolution - 24 MEVD maps at point scale (downscaled) Project Information These results are part of the INTENSE (raINfall exTremEs and their impacts: from the local to the National ScalE) project. Project website: https://intenseproject.uniud.it/ Funding This research was supported by the "raINfall exTremEs and their impacts: from the local to the National ScalE" (INTENSE) project, funded by the European Union – Next Generation EU within the framework of the PRIN (Progetti di ricerca di Rilevante Interesse Nazionale) programme (grant 2022ZC2522). References Von Mises, R. (1936). La distribution de la plus grande de n valeurs, Rev. Math. Union Interbalcanique, 1, 141–160. Marani, M., and M. Ignaccolo. (2015). A metastatistical approach to rainfall extremes, Adv. Water Resour., 79, 121–126. Zorzetto, E., Marani, M. (2019). Downscaling of rainfall extremes from satellite observations. Water Resour. Res. 55 (1), 156–174.