mlsanityData to 14 Oct 2026

AI-boosted rare event sampling to characterize extreme weather

arXiv 2510.27066 ↗First posted 31 Oct 2025physics.ao-ph stat.CO stat.MLcited by 3

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Abstract (arXiv)

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 heatwaves at two orders-of-magnitude lower computational cost. AI+RES can be applied broadly across climate science and other fields concerned with rare events.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 79 references that are in the map. First posted on arXiv 31 Oct 2025.

Builds on

15 of 79 references.

References · 15

  1. 2022FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorscited by 776
  2. 2023Neural General Circulation Models for Weather and Climatecited by 239
  3. 2025Skillful joint probabilistic weather forecasting from marginalscited by 63
  4. 2024ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responsescited by 54
  5. 2019Analog forecasting of extreme-causing weather patterns using deep learningcited by 33
  6. 2024ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecastingcited by 32
  7. 2023Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solutioncited by 31
  8. 2024Can AI weather models predict out-of-distribution gray swan tropical cyclones?cited by 26
  9. 2025Numerical models outperform AI weather forecasts of record-breaking extremescited by 14
  10. 2022Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of datacited by 7
  11. 2025Probing forced responses and causality in data-driven climate emulators: conceptual limitations and the role of reduced-order modelscited by 6
  12. 2024Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwavescited by 4
3 more
  1. 2023Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learningcited by 3
  2. 2025Reframing Generative Models for Physical Systems using Stochastic Interpolantscited by 2
  3. 2017Convergence of extreme value statistics in a two-layer quasi-geostrophic atmospheric modelcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Conditional Tropical Cyclogenesis Rates via Rare-Event Sampling in a Neural Weather Emulatorcited by 1
  2. 2026Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?cited by 0
  3. 2026Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Modelscited by 0