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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.26359 |
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| _version_ | 1866911632219701248 |
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| author | Gasser, Kamal Segers, Johan Ragone, Francesco |
| author_facet | Gasser, Kamal Segers, Johan Ragone, Francesco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26359 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A spatio-temporal statistical framework for heatwave attribution under climate change Gasser, Kamal Segers, Johan Ragone, Francesco Applications Methodology 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. |
| title | A spatio-temporal statistical framework for heatwave attribution under climate change |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2604.26359 |