📝 SummarXiv

Abstract

Extreme precipitation events occurring over large spatial domains pose substantial threats to societies because they can trigger compound flooding, landslides, and infrastructure failures across wide areas. A hybrid framework for spatial extreme precipitation modeling and risk zoning is proposed that integrates graph neural networks with r-Pareto processes (GNN-rP). Unlike traditional statistical spatial extremes models, this approach learns nonlinear, nonstationary dependence structures from precipitation-derived spatial graphs and applies a data-driven tail functional to model joint exceedances in a low-dimensional embedding space. Using NASA's IMERG observations (2000-2021) and CMIP6 SSP5-8.5 projections, the framework delineates coherent high-risk zones, quantifies their temporal persistence, and detects emerging hotspots under climate change. Compared with two baseline approaches, the GNN-rP pipeline substantially improves pointwise detection of high-risk grid cells while yielding comparable clustering stability. Results highlight persistent high-risk regions in the tropical belt, especially monsoon and convective zones, and reveal decadal-scale persistence that is punctuated by episodic reconfigurations under high-emission scenarios. By coupling machine learning with extreme value theory, GNN-rP offers a scalable, interpretable tool for adaptive climate risk zoning, with direct applications in infrastructure planning, disaster preparedness, and climate-resilient policy design.