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.

翻译：我们提出了一个统一的统计框架，用于将热浪视为气候变化下的时空现象进行归因分析。我们量化了人为强迫对热浪发生概率和持续性的影响，这种影响无法通过标准边际极值方法捕捉。该方法构建了一个每日温度场的生成模型，将边际非平稳性与时空依赖性分离。我们结合了三个组成部分：用于数据主体部分的贝叶斯空间分位数回归模型；用于尾部分布的非平稳空间广义极值模型；以及用于捕捉极端事件中渐近依赖性和独立性的copula模型。该框架应用于CMIP6 MRI-ESM2气候模型，通过对比事实与反事实情景进行概率归因。结果表明，该方法能够捕捉传统方法无法获取的关键热浪特征，从而直接估算事件级归因指标。总体而言，它为分析复杂气候极端事件（作为时空对象）并对其进行归因提供了灵活基础。