High quantile estimation and spatial aggregation applied to precipitation extremes

We shall address the problem of high quantile estimation in univariate and spatial Extreme Value Theory. Univariate methods are well known under the Maximum Domain of Attraction Condition and the Pareto tail approximation is the basis for many estimators. It turns out that the Pareto tail approximation is also valid under spatial aggregation but a spatial effect comes out. We shall address the problem both theoretically and in practice, by presenting a case study on 100-year return value estimation for precipitation data collected at rain-gauge stations.