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

TL;DR

This paper introduces a comprehensive statistical framework for attributing heatwaves to climate change, capturing their spatio-temporal dynamics and extreme event characteristics.

Contribution

It presents a novel unified model combining Bayesian quantile regression, extreme value theory, and copulas for heatwave attribution under climate change.

Findings

Framework captures heatwave characteristics beyond traditional methods

Enables direct estimation of event-level attribution metrics

Applied to CMIP6 data contrasting factual and counterfactual scenarios

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.