Global Mapping of Temperature-Health Risks: A Model of AI for Planetary Health

Temperature anomalies drive substantial excess mortality, yet existing early warning systems remain limited to regional scales, reliant on linear assumptions, and fail to adequately account for multi-dimensional thermal stress and socioeconomic heterogeneity. This study develops the Planetary Health Axis System–Meteorology (PHAS–M), a framework designed to transform sub-daily weather forecasts into location-specific predictions of the risk of temperature-related excess mortality.PHAS-M employs a Bayesian, prior-informed severity–exposure–vulnerability decomposition coupled with a copula model to capture non-linear mechanisms and spatial variation in adaptive capacity. In validation, it dramatically outperforms existing approaches and surpasses both conventional regression and pure machine learning baselines.This methodology further reveals that the heterogeneity of temperature-induced health risks is attributable to socioeconomic vulnerability, and supports the integration of a broader set of heterogeneous characteristics into predictive climate–health models. The PHAS-M framework provides an interpretable and universal operational tool for decision-makers to better intervene in weather-related health risks.