Estimates of Point Rainfall Extremes from Satellite Precipitation Products: Application and bias correction in Italy

Reliable estimates of extreme precipitation are essential for understanding, predicting, and mitigating natural disasters. However, global-scale assessments are limited by the sparse and uneven distribution of ground-based observations. Satellite-based rainfall products provide valuable support for extreme value analysis, but their applicability is constrained by high uncertainty and coarse spatial resolution. The coarse resolution of global datasets (100–600 km² grids) prevents direct comparison with point-scale extreme value estimates, as point and area-averaged statistics differ inherently.

This study addresses this limitation by applying a downscaling approach for extreme-value statistics based on random field theory and the Metastatistical Extreme Value Distribution (MEVD). The method exploits the autocorrelation structure of precipitation fields and is applied to each product at grid cells corresponding to rain gauge locations. Six remote sensing and reanalysis (RSR) products, along with their ensemble, are evaluated using a rain gauge network in Italy.

Downscaled estimates of daily 50-year return period precipitation are compared with corresponding estimates derived from rain gauge time series, considering both individual products and their ensemble median. To further improve the accuracy of satellite maps, two bias correction techniques are applied: quantile mapping and linear regression. The final results show that the ensemble obtained from the median of the RSR products provides the best overall performance.

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 in the framework of PRIN (Progetti di ricerca di Rilevante Interesse Nazionale) programme (grant 2022ZC2522). Marco Marani was also supported by the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).