Preprints
https://doi.org/https://doi.org/10.48550/arXiv.2506.10660
https://doi.org/https://doi.org/10.48550/arXiv.2506.10660
12 Aug 2025
 | 12 Aug 2025
Status: this preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).

Constructing Extreme Heatwave Storylines with Differentiable Climate Models

Tim Whittaker and Alejandro Di Luca

Abstract. Understanding the plausible upper bounds of extreme weather events is essential for risk assessment in a warming climate. Existing methods, based on large ensembles of physics-based models, are often computationally expensive or lack the fidelity needed to simulate rare, high-impact extremes. Here, we present a novel framework that leverages a differentiable hybrid climate model, NeuralGCM, to optimize initial conditions and generate physically consistent worst-case heatwave trajectories. Applied to the 2021 Pacific Northwest heatwave, our method produces heatwave intensity up to 3.7 °C above the most extreme member of a 75-member ensemble. These trajectories feature intensified atmospheric blocking and amplified Rossby wave patterns—hallmarks of severe heat events. Our results demonstrate that differentiable climate models can efficiently explore the upper tails of event likelihoods, providing a powerful new approach for constructing targeted storylines of extreme weather under climate change.

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Tim Whittaker and Alejandro Di Luca

Status: open (until 05 Oct 2025)

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Tim Whittaker and Alejandro Di Luca
Tim Whittaker and Alejandro Di Luca

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Short summary
Heatwaves are becoming more extreme in frequency and intensity. Yet running many climate simulations to find the rare worst-case events is slow and costly. We developed a method that tweaks initial weather conditions to target the most extreme heat scenarios at a fraction of the usual cost. For the 2021 Pacific Northwest heatwave, it found cases up to 3.7 °C hotter than any run in a 75-member ensemble, helping communities prepare for the worst.
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