A Storyline-based Evaluation of Data-Driven Weather Models for Simulating the 2019 European Summer Heatwave under Future Warming Scenarios

Recently developed data-driven weather models demonstrate accuracy comparable to physical models while being computationally cheaper. Nonetheless, their efficacy in modeling extreme weather conditions in present day and their applicability in future warmer scenarios remains largely unknown. This study evaluates the performance of various global data-driven weather models in predicting the peak of the 2019 European summer heatwave, compared to the physics-based ICON model. The evaluation not only encompasses the present climate but also aims to assess the performance of these models in emulating this specific event in a +2, +3, and +4K warmer world relative to pre-industrial levels, following a storyline framework. Both data-driven and ICON models are initialized with global storyline simulations from the AWI-CM1 model for current and future warmer scenarios. To quantify the sensitivity to initial condition uncertainties, the random field perturbation technique is employed in the data-driven models. The findings indicate that the majority of data-driven models inadequately predict maximum temperatures in the heatwave core region under present climatic conditions as compared to ICON, exhibiting an underestimation of up to 2°C, even within an ensemble framework. These models are also assessed against ICON under future warmer climatic scenarios to determine their generalization capabilities outside the training distribution. The results indicate that these models can capture temperature amplification across climate scenarios to some extent, they underestimate the accelerated warming rate during the heat wave compared to ICON. This highlights both the potential and limitations of data-driven models for their applicability in future warmer scenarios and emphasizing the necessity to develop hybrid strategies combining physics with data-driven techniques to enhance predictive accuracy in a changing climate.