Enhancing seasonal forecast of health-related heat-stress indicators through teleconnection-based subsampling

In recent years, hybrid statistical-dynamical approaches have emerged as a promising avenue to enhance seasonal predictions of the extratropical climate (Slater et al., 2023). Among these, the teleconnection-based subsampling has been shown to significantly improve seasonal predictions of Eurasian climate, including the occurrence of summer extreme temperatures (Famooss Paolini et al., 2024). This technique relies on selecting a subset of ensemble members whose predictions of summer low-frequency atmospheric variability are consistent with its statistical forecasts from springtime predictors.

Here, we assess the potential of the teleconnection-based subsampling to enhance seasonal predictions of two health-related heat-stress indicators in summer: the Universal Thermal Climate Index (UTCI) and the Wet Bulb Globe Temperature (WBGT), which combine information on temperature, humidity, radiation and wind. The methodology is implemented to mimic real-time operational forecast environment, thus differing from standard retrospective forecast (hindcast) applications. We use the ECMWF seasonal prediction system initialised on May 1 and ERA5 reanalysis as surrogate of observations, assessing the prediction skill during 2003—2024. The ECMWF ensemble is subsampled by retaining only those ensemble members that best capture the teleconnection patterns associated with the summer North Atlantic Oscillation (NAO), the leading mode of summer low-frequency atmospheric variability over the North Atlantic sector.

Our results show that the teleconnection-based subsampling in operational forecast environment increases seasonal prediction skill of the summer NAO, moving from near-zero correlation for the full ECMWF ensemble to about 0.45 for the subsampled ECMWF ensemble. In turn, constraining the ensemble to members with a realistic NAO phase enhances the prediction skill of both the UTCI and WBGT, particularly over the Scandinavia and western Europe, where the summer NAO exerts its strongest influence on heat-stress conditions. Results also show that this improvement is mainly linked to a better representation of thermal conditions rather than wind in those regions.

These findings are particularly relevant, as they contribute to the development and implementation of innovative methodologies for predicting climate conditions that pose risks to human health. This is a key priority in the context of climate change, which is projected to substantially increase heat-related mortality unless strong mitigation and adaptation strategies are adopted (Masselot et al., 2025).

Bibliography

Famooss Paolini et al. (2024). Hybrid statistical-dynamical seasonal prediction of summer extreme temperatures in Europe. Quarterly Journal of the Royal Meteorological Society, 151(766). https://doi.org/10.1002/qj.4900

Masselot et al. (2025). Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities. Nature Medicine, 1-9.  https://doi.org/10.1038/s41591-024-03452-2

Slater et al. (2023). Hybrid forecasting: blending climate predictions with AI models. Hydrology and earth system sciences, 27(9), 1865-1889. https://doi.org/10.5194/hess-27-1865-2023