Are spatial precipitation extremes becoming more intense, wider, or both? An extreme-value statistics perspective

Abstract

Extreme precipitation events with large spatial extents may have more severe impacts than localized events as they can lead to widespread flooding. It is debated how climate change may affect the spatial extent of precipitation extremes, whose investigation often directly relies on simulations from climate models. Here, we use a different strategy to investigate how future changes in spatial extents of precipitation extremes differ across climate zones and seasons in two river basins (Danube and Mississippi). We rely on observed precipitation extremes while exploiting a physics-based mean temperature covariate, which enables us to project future precipitation extents. We include the covariate into newly developed time-varying r-Pareto processes using a suitably chosen spatial aggregation functional r. This model captures temporal non-stationarity in the spatial dependence structure of precipitation extremes by linking it to the temperature covariate, which we derive from observations for model calibration and from bias corrected climate simulations (CMIP6) for projections. Then, we fit the model for both river basins and make projections into the future under different climate warming scenarios.