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

Kamal Gasser; Johan Segers; Francesco Ragone

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arXiv · 2604.26359

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

Kamal Gasser·Johan Segers·Francesco Ragone

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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.

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Kamal Gasser, Johan Segers, Francesco Ragone. 2026-04-29. A spatio-temporal statistical framework for heatwave attribution under climate change. https://arxiv.org/abs/2604.26359

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