A spatio-temporal statistical framework for heatwave attribution under climate change

2026-04-29

AI Summary

Traditional extreme value methods struggle to characterize the probabilistic features of heatwaves as spatiotemporally coherent events and their response to anthropogenic climate change. This study proposes a unified statistical framework that treats heatwaves as spatiotemporal phenomena for attribution analysis, jointly modeling the marginal nonstationarity of temperature fields and the asymptotic dependence or independence structure of extremes for the first time. The approach integrates Bayesian spatial quantile regression, nonstationary spatial generalized extreme value modeling, and copula-based extreme dependence modeling to directly estimate event-level attribution metrics. Applied to CMIP6 MRI-ESM2 data, the framework successfully captures key heatwave characteristics overlooked by conventional methods, substantially enhancing the accuracy and flexibility of extreme event attribution in a changing climate.

Abstract

We develop a unified statistical framework for attributing heatwaves as spatio-temporal phenomena under climate change. We quantify the impact of anthropogenic forcing on the probability and persistence of heatwaves not captured by standard marginal extreme-value approaches. Our methodology constructs a generative model for daily temperature fields that separates marginal nonstationarity from spatio-temporal dependence. We combine three components: a Bayesian spatial quantile regression model for the bulk of the data; a nonstationary spatial generalized extreme value model for tail behavior; and a copula-based model capturing both asymptotic dependence and independence in the extremes. The framework is applied to the CMIP6 MRI-ESM2 climate model, contrasting factual and counterfactual scenarios for probabilistic attribution. Our results show that the approach captures key heatwave characteristics inaccessible to traditional methods, enabling direct estimation of event-level attribution metrics. Overall, it provides a flexible basis for analyzing and attributing complex climate extremes as space-time objects.

Problem

• heatwave attribution
• climate change
• spatio-temporal extremes
• anthropogenic forcing
• extreme-value analysis

Innovation

• spatio-temporal modeling
• heatwave attribution
• nonstationary extremes
• copula-based dependence
• Bayesian quantile regression