• Analysis shows the G E V distribution can underestimate precipitation extremes. • G E V + and B r X I I describe more consistently extreme precipitation than the G E V . • Maps of rainfall depths for different return periods are provided for Italy. Italy. Knowing magnitude and frequency of extreme precipitation is necessary to reduce their impact on vulnerable areas. Here we investigate the performance of the Generalized Extreme Value ( G E V ) distribution, using a fine-resolution satellite-based gridded product, to analyze 13,247 daily rainfall annual maxima samples. A non-extreme value distribution with a power-type behavior, that is, the Burr Type XII ( B r X I I ), is also evaluated and used to test the reliability of the G E V in describing extreme rainfall. (1) in 44.9 % of the analyzed samples the G E V predicts an upper rainfall limit; we deem this is an artifact due to sample variations; (2) we suggest the G E V + distribution, that is, the G E V with shape parameters restricted only to positive values as a more consistent model complying with the nature of extreme precipitation; (3) G E V , G E V + , and B r X I I performed equally well in describing the observed annual precipitation, yet all distributions underestimate the observed sample maximum; (4) the B r X I I , for large return periods, predicts larger rainfall amounts compared to G E V indicating that G E V estimates could underestimate the risk of extremes; and (5) the correlation between the predicted rainfall and the elevation is investigated. Based on the results of this study, we suggest instead of using the classical G E V to use the G E V + and non-extreme value distributions such as the B r X I I to describe precipitation extremes.

• Analysis shows the G E V distribution can underestimate precipitation extremes. • G E V + and B r X I I describe more consistently extreme precipitation than the G E V . • Maps of rainfall depths for different return periods are provided for Italy. Italy. Knowing magnitude and frequency of extreme precipitation is necessary to reduce their impact on vulnerable areas. Here we investigate the performance of the Generalized Extreme Value ( G E V ) distribution, using a fine-resolution satellite-based gridded product, to analyze 13,247 daily rainfall annual maxima samples. A non-extreme value distribution with a power-type behavior, that is, the Burr Type XII ( B r X I I ), is also evaluated and used to test the reliability of the G E V in describing extreme rainfall. (1) in 44.9 % of the analyzed samples the G E V predicts an upper rainfall limit; we deem this is an artifact due to sample variations; (2) we suggest the G E V + distribution, that is, the G E V with shape parameters restricted only to positive values as a more consistent model complying with the nature of extreme precipitation; (3) G E V , G E V + , and B r X I I performed equally well in describing the observed annual precipitation, yet all distributions underestimate the observed sample maximum; (4) the B r X I I , for large return periods, predicts larger rainfall amounts compared to G E V indicating that G E V estimates could underestimate the risk of extremes; and (5) the correlation between the predicted rainfall and the elevation is investigated. Based on the results of this study, we suggest instead of using the classical G E V to use the G E V + and non-extreme value distributions such as the B r X I I to describe precipitation extremes.