An Attention-Based Stochastic Simulator for Multisite Extremes to Evaluate Nonstationary, Cascading Flood Risk

Compound flood risks from spatially and temporally clustered extremes challenge traditional risk models and insurance portfolios that often neglect correlated risks across regions. Spatiotemporally clustered floods exhibit fat-tail behavior, modulated by low-frequency hydroclimatic variability and large-scale moisture transport. Nonstationary stochastic simulators and regional compound event models aim to capture such tail risk, but have not yet unified spatial and temporal extremes under low-frequency hydroclimatic variability. We introduce a novel attention-based framework for multisite flood generation conditional on a multivariate hydroclimatic signal with explainable attribution to global sub-decadal to multi-decadal climate variability. Our simulator combines wavelet signal processing, transformer-based multivariate time series forecasting, and modified Neyman-Scott joint clustering to simulate climate-informed spatially compounding and temporally cascading floods. Applied to a Mississippi River Basin case study, the model generates distributed portfolios of plausibly clustered flood risks across space and time, providing a basis for simulating spatiotemporally correlated losses characteristic of flood-induced damage.