AI Weather Models Race Physics Models to Predict Heat Deaths in Europe

Europe’s summers have become deadly on a scale that demands better forecasting. An estimated 62,000 heat-related deaths occurred across the continent during the summer of 2024, following similarly grim tolls in the two preceding summers. Health authorities rely on early warning systems to trigger protective measures, from opening cooling centres to alerting hospitals, but the quality of those warnings depends entirely on the weather forecasts beneath them. Now, a study published in Natural Hazards and Earth System Sciences by Emma Holmberg and Leonardo Olivetti of Uppsala University has put two rival forecasting philosophies head to head, asking a question that goes beyond meteorology: which kind of weather model better predicts not the temperature itself, but the deaths it causes?

The contest pits two fundamentally different approaches against each other. Physics-based forecasts come from numerical weather prediction models, the traditional workhorses of meteorology, which solve equations describing atmospheric dynamics, radiation, and ocean and land-surface processes on a discretised grid. Data-driven forecasts, by contrast, are built by artificial intelligence systems trained on vast archives of historical weather data, typically reanalysis datasets such as ERA5, which blend observations with model output to create a consistent record of past atmospheric states. In just a few years, data-driven models have achieved improvements in forecast skill that took physics-based approaches decades, and they now rival or beat numerical models on standard metrics, including for some extreme events. Yet important weaknesses remain: AI models can struggle with extremes, and their training loss functions tend to prioritise performance near the centre of the climatological distribution rather than in the tails, where the stakes are highest.

That tail behaviour matters enormously here, because the relationship between temperature and mortality is strongly non-linear. Deaths rise disproportionately once temperatures climb past a location-specific threshold known as the minimum mortality temperature, so a forecast with a small average temperature error can still produce a badly wrong estimate of health impacts if that error is concentrated on the hottest days. The researchers exploited this non-linearity deliberately, using an epidemiological transformation as an explicitly impact-oriented error metric. Rather than comparing forecast temperatures against observations, they converted both forecasts and reanalysis data into estimates of the attributable fraction of mortality, the proportion of deaths statistically attributable to non-optimal temperatures, and compared those impact estimates instead.

The epidemiological machinery behind this conversion is well established. The study drew on exposure-response functions fitted for 854 European cities across 30 countries by a large health impact assessment published in The Lancet Planetary Health in 2023. Those fits were built with distributed lag non-linear models, a two-stage framework in which a quasi-Poisson regression first captures the over-dispersed relationship between temperature and all-cause mortality, including its complex lagged structure through a cross-basis function, and a multivariate meta-regression then pools the city-level estimates while accounting for differences in vulnerability between cities. The relative risk of dying at a given temperature is expressed as a ratio against the risk at the minimum mortality temperature, and the attributable fraction follows directly from that ratio.

For the meteorological side, the authors used the two flagship forecast systems of the European Centre for Medium-Range Weather Forecasts, widely considered among the best in the world. The physics-based contender was the Integrated Forecasting System high-resolution model, cycle 48r1, while the data-driven contender was the Artificial Intelligence Forecasting System, single v1. Both were evaluated deterministically at lead times of one, three, five, seven and ten days over the boreal summer of 2024, from 1 June to 31 August, with ERA5 reanalysis serving as the ground truth. The summer of 2024 was chosen both because it offered the most recent model versions and because it was the hottest summer on record globally and for Europe, making it a realistic test bed for the conditions under which health-impact forecasts would actually be used operationally.

The results begin with a telling case study of Rome, selected because Italy reported Europe’s highest heat-related death toll in 2024. In Rome, the data-driven forecasts actually achieved lower mean absolute errors for temperature than the physics-based forecasts, yet their bias in the attributable fraction was larger in magnitude at every lead time examined. This divergence between temperature skill and impact skill is the study’s central lesson: the two quantities tell qualitatively different stories. A modest warm bias in temperature can translate into a substantial overestimate of mortality risk, while a cool bias on a scorching day can lull health authorities into underestimating a lethal event. The non-linear transformation amplifies errors precisely where impacts are greatest.

Scaled up to the continental level using population-weighted averages across all 854 cities, the picture sharpened. Both forecast types systematically underestimated temperature-related mortality, but the underestimation was more pronounced for the data-driven forecasts, particularly at hot temperatures where the forecasts would matter most for warning systems. Quantile-quantile analysis showed both models falling systematically below the zero-error line for attributable fraction, and Kolmogorov-Smirnov tests confirmed that the error distributions of the two models were statistically distinct at lead times of three, five and seven days. Scatter plots of forecast bias against temperature revealed negative correlations: the hotter the day, the more the forecasts underpredicted the health burden. For the physics-based model this underestimation emerged only at exceptionally hot temperatures, consistent with known difficulties in representing land-atmosphere coupling, soil moisture deficits and urban effects during European heatwaves.

The lead-time behaviour added a further twist. Temperature forecast errors grew with lead time as expected, but the errors in attributable fraction did not show a comparably clean growth pattern, a finding the authors attribute to the non-linear propagation of temperature errors through the epidemiological transformation, combined with the limited sample of a single summer. The data-driven model showed its largest systematic underestimation of mortality near the end of the first forecast week, close to the predictability limit for these impact metrics. Interestingly, the mean absolute error of the impact forecasts was nearly identical between the two models up to five days ahead, suggesting that for practical warning purposes on short timescales, the AI model is already competitive.

What makes these findings compelling is their practical upside. Data-driven forecasts are dramatically cheaper to run than numerical models, which demand supercomputing resources that many national weather services and health agencies lack. If the systematic underestimation of heat mortality in AI forecasts can be corrected, bespoke impact forecasts could be produced at low cost in resource-limited settings, extending life-saving early warning capability to places that currently have none. The authors propose two concrete remedies: a temperature-dependent bias correction applied after the forecast, and a modification of the training loss function to give greater weight to hot temperatures, so the model learns that errors in the heat matter more than errors elsewhere in the distribution.

The study’s limitations are acknowledged candidly: a single summer cannot establish generalisability, only two global deterministic models were tested rather than regional systems or ensembles, and applying epidemiological fits to temperatures outside their calibration range introduces uncertainty at the very hottest extremes. Extending the comparison to ensemble forecasts, to recent probabilistic AI models trained to represent full distributions rather than single values, and to other regions and health outcomes are flagged as the natural next steps. Even so, the verdict is cautiously optimistic. Temperature-related mortality forecasts built on data-driven weather models performed approximately as well as those built on physics-based models, and with targeted improvements to how hot temperatures are represented, they could become a promising foundation for the next generation of heat-health early warning systems, at a moment when Europe can ill afford to miss another deadly summer.

Subject of Research: Comparison of data-driven and physics-based weather forecasts for predicting temperature-related mortality in Europe during summer 2024

Article Title: Forecasting European temperature-related mortality in Summer 2024: data-driven vs. physics-based forecast approaches

Article References: Holmberg, E., & Olivetti, L. (2026). Forecasting European temperature-related mortality in Summer 2024: data-driven vs. physics-based forecast approaches. Natural Hazards and Earth System Sciences, 26(10), 4825-4842. https://doi.org/10.5194/nhess-26-4825-2026

Image Credits: AI Generated

DOI: 10.5194/nhess-26-4825-2026

Keywords: heat-related mortality, data-driven weather forecasting, numerical weather prediction, early warning systems, Europe, summer 2024, attributable fraction, ECMWF, AIFS, extreme heat, epidemiological forecasting, machine learning