{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "CNRS",
    "ENS",
    "IPSL",
    "Institut Polytechnique de Paris",
    "LMD",
    "Réseau de Transport d’Électricité (RTE)",
    "Sorbonne Université",
    "Université PSL",
    "École Polytechnique",
    "University of Chicago",
    "World Energy & Meteorology Council (WEMC)",
    "Courant Institute",
    "New York University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5,
        6,
        7,
        8
      ],
      "name": "Amaury Lancelin",
      "orcid": "0009-0005-3898-3043"
    },
    {
      "affs": [
        9
      ],
      "name": "Alexander Wikner",
      "orcid": "0000-0001-7575-3953"
    },
    {
      "affs": [
        5,
        10
      ],
      "name": "Laurent Dubus",
      "orcid": "0000-0002-3987-646X"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        6,
        7,
        8
      ],
      "name": "Clément Le Priol",
      "orcid": "0000-0002-8214-693X"
    },
    {
      "affs": [
        9
      ],
      "name": "Dorian S. Abbot",
      "orcid": "0000-0001-8335-6560"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        6,
        7,
        8
      ],
      "name": "Freddy Bouchet",
      "orcid": "0000-0002-1623-0818"
    },
    {
      "affs": [
        9
      ],
      "name": "Pedram Hassanzadeh",
      "orcid": "0000-0001-9425-8085"
    },
    {
      "affs": [
        11,
        12
      ],
      "name": "Jonathan Weare",
      "orcid": "0000-0001-8745-1821"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/88jyycqfsbmxf.bib",
  "cited_by": 19,
  "doi": "10.1103/b1gc-9c2q",
  "fulltext_url": null,
  "id": "88jyycqfsbmxf",
  "issns": [
    "0031-9007",
    "1079-7114",
    "1092-0145"
  ],
  "landing_url": "https://doi.org/10.1103/b1gc-9c2q",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W4415914908",
  "pdf_url": null,
  "pmcid": null,
  "pmid": "42628090",
  "published": "2026-06-08",
  "publisher": "American Physical Society",
  "ris": "https://research.draconitic.com/paper/88jyycqfsbmxf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:57Z",
      "native_id": "W4415914908",
      "source": "openalex",
      "url": "https://doi.org/10.1103/b1gc-9c2q"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1902|Meteorological Phenomena and Simulations",
      "subfield": "atmospheric"
    }
  ],
  "title": "AI-Boosted Rare Event Sampling to Characterize Extreme Weather",
  "title_text": "AI-Boosted Rare Event Sampling to Characterize Extreme Weather",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:19Z",
  "url": "https://research.draconitic.com/paper/88jyycqfsbmxf",
  "venue": "Physical Review Letters",
  "versions": []
}