Articles | Volume 7, issue 1
https://doi.org/10.5194/wcd-7-393-2026
https://doi.org/10.5194/wcd-7-393-2026
Research article
 | 
19 Feb 2026
Research article |  | 19 Feb 2026

Constructing extreme heatwave storylines with differentiable climate models

Tim Whittaker and Alejandro Di Luca

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Cited articles

Alet, F., Price, I., El-Kadi, A., Masters, D., Markou, S., Andersson, T. R., Stott, J., Lam, R., Willson, M., Sanchez-Gonzalez, A., and Battaglia, P.: Skillful joint probabilistic weather forecasting from marginals, arXiv [preprint], https://doi.org/10.48550/arXiv.2506.10772, 12 June 2025. a
Baño-Medina, J., Sengupta, A., Doyle, J. D., Reynolds, C. A., Watson-Parris, D., and Monache, L. D.: Are AI weather models learning atmospheric physics? A sensitivity analysis of cyclone Xynthia, npj Clim. Atmos. Sci., 8, 92, https://doi.org/10.1038/s41612-025-00949-6, 2025. a
Bartusek, S., Kornhuber, K., and Ting, M.: 2021 North American heatwave amplified by climate change-driven nonlinear interactions, Nat. Clim. Change, 12, 1143–1150, https://doi.org/10.1038/s41558-022-01520-4, 2022. a
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06185-3, 2023. a
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q.: JAX: composable transformations of Python+NumPy programs, GitHub [code], http://github.com/jax-ml/jax, 2018. a
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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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