Abstract. Estimation of extreme event distributions and depth-duration-frequency (DDF) curves is achieved at any target site by repeated sampling among all available raingauge data in the surrounding area. The estimate is computed over a gridded domain in Northern Italy, using precipitation time series from 1929 to 2011, including data from historical analog stations and from the present-day automatic observational network. The presented local regionalisation naturally overcomes traditional station-point methods, with their demand of long historical series and their sensitivity to very rare events occurring at very few stations, possibly causing unrealistic spatial gradients in DDF relations. At the same time, the presented approach allows for spatial dependence, necessary in a geographical domain such as Lombardy, complex for both its topography and its climatology. The bootstrap technique enables evaluating uncertainty maps for all estimated parameters and for rainfall depths at assigned return periods.

Estimation of extreme event distributions and depth-duration-frequency (DDF) curves is achieved at any target site by repeated sampling among all available raingauge data in the surrounding area. The estimate is computed over a gridded domain in Northern Italy, using precipitation time series from 1929 to 2011, including data from historical analog stations and from the present-day automatic observational network. The presented local regionalisation naturally overcomes traditional station-point methods, with their demand of long historical series and their sensitivity to very rare events occurring at very few stations, possibly causing unrealistic spatial gradients in DDF relations. At the same time, the presented approach allows for spatial dependence, necessary in a geographical domain such as Lombardy, complex for both its topography and its climatology. The bootstrap technique enables evaluating uncertainty maps for all estimated parameters and for rainfall depths at assigned return periods