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Name: Mark Risser

Talk Title: Detecting changes in daily precipitation extremes over the contiguous United States

Abstract: In spite of the diverse literature on spatial extreme value analysis, characterizing trends in extremes of an environmental process like daily precipitation for a large network of monitoring stations over a heterogeneous spatial domain remains a challenging statistical problem. Here, we compare and contrast two methods that are scalable to high-dimensional, heterogenous spatial data sets, namely conditional independence-based GEV methods that utilize bootstrapping and hierarchical Bayesian methods for block maxima. Both approaches are used to detect long-term trends in precipitation extremes over the contiguous United States for a large network of several thousand weather stations. Locally, in spite of significant noise, we are able to detect statistically significant trends in seasonal precipitation extremes, generally resulting in larger and more frequent extreme events, although there are also important areas where the opposite is true.

This talk is an invited talk at EVA 2021. View the programme here.