Mortality under locally unprecedented heat is better estimated with cross-regional learning

Résumé fourni par la source

Abstract Climate change is pushing many regions across Europe into heat conditions beyond local historical experience, challenging heat-mortality models primarily calibrated on local observations. These locally learned exposure-response relationships are often weakest precisely where extreme heat has been rare and risk is now emerging. Here, we show that jointly learning temperature-mortality relationships across regions using a shared European exposure space improves estimation of excess mortality under locally unprecedented heat events and enhances near-term, out-of-sample predictive performance. By borrowing strength across regions with heterogeneous heat exposure, this approach replaces local extrapolation beyond observed temperatures with interpolation across a broader range of observed exposures. Joint learning yields the largest gains in cooler climates with historically rare heat exposure (mean increase in pseudo- R 2 = 0.09 , IQR [ 0.07 − 0.13 ] ) and in regions experiencing temperatures exceeding local past maxima. Importantly, the learned relationships remain epidemiologically interpretable and stable when incorporating concurrent environmental exposures, enabling assessment of modifiers such as humidity while preserving coherent temperature-mortality relationships. As climate change continues to push populations into locally unprecedented heat conditions, these strengths support the use of joint learning for the development of early warning systems and frameworks for the near-term forecasting of heat-related mortality.

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