{
  "abstract": "Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasting, medium-range, and subseasonal-to-seasonal lead times, producing well-calibrated ensemble forecasts at reduced cost. We review these advances and consider their extension to climate horizons, where the challenge shifts from initial-condition skill to producing reliable statistical responses under altered forcings. AI-powered climate prediction systems must produce credible forced responses to drivers (e.g., greenhouse gases, land-use change) typically outside the observed record. We propose two minimum requirements for AI in climate modeling: (i) external forcing agents must enter explicitly enough to support interventions in which they vary independently; and (ii) robustness must be stress-tested in out-of-distribution regimes, including extremes and counterfactual trajectories. Using leading AI autoregressive emulators and hybrid physics-AI models, we identify development and coupling challenges. Comparing the reported throughput of these models with that of GPU-ported dynamical models highlights how AI can reduce time-to-solution by advancing only the target variables at the required resolution and using longer time steps, rather than integrating a full high-frequency, multivariate state. Diverse AI downscaling strategies can partially substitute for explicit fine-scale resolution, paving the way toward inexpensive local hazard assessment across prediction horizons.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.09770",
  "author_total": 13,
  "authors": [
    {
      "name": "Tom Beucler"
    },
    {
      "name": "J. David Neelin"
    },
    {
      "name": "Hui Su"
    },
    {
      "name": "Shivanshi Asthana"
    },
    {
      "name": "Chris Bretherton"
    },
    {
      "name": "Will Chapman"
    },
    {
      "name": "Costa Christopoulos"
    },
    {
      "name": "Spencer K. Clark"
    },
    {
      "name": "Aditya Grover"
    },
    {
      "name": "Ignacio Lopez-Gomez"
    },
    {
      "name": "Tapio Schneider"
    },
    {
      "name": "Adam Subel"
    },
    {
      "name": "Oliver Watt-Meyer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6j0haqtmephnv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.09770",
  "fulltext_url": null,
  "id": "6j0haqtmephnv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.09770",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7220946637",
  "pdf_url": "https://arxiv.org/pdf/2610.09770",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6j0haqtmephnv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2610.09770",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.09770"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Artificial intelligence pathways from weather to climate",
  "title_text": "Artificial intelligence pathways from weather to climate",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/6j0haqtmephnv",
  "venue": "arXiv",
  "versions": []
}