{
  "abstract": "Physics-based models provide skillful subseasonal forecasts but suffer from systematic biases, weakened teleconnections, and parameterized physics, while very large ensembles remain computationally expensive. Purely data-driven models also see too few realizations of the low-frequency modes that provide subseasonal predictability. We introduce methodname, a hybrid framework that converts modelshort subseasonal ensemble guidance into 90-member stochastic forecasts of weekly precipitation and 2 m temperature on a global 1-degree grid using a lead-aware flow-matching U-Net. Its stochastic prior initializes members with EOF perturbations conditioned on the Madden-Julian Oscillation, North Atlantic Oscillation, El Nino-Southern Oscillation, and lead week, allowing the learned flow to refine large-scale covariance rather than create it from white noise. On held-out 2021-2025 forecasts, empirical CRPS improves by 22.0% for precipitation and 25.6% for temperature in a same-initialization four-versus-four comparison, and by 32.2% and 34.0% in the 90-versus-four production comparison. Ablations across five training seeds show distinct gains from the structured prior and a learned error-variance pathway, with the prior contributing more. Verification over 120 observed-extreme cases shows positive CRPS skill, but the ensemble remains underdispersive, four-member upper quantiles are poorly resolved, and precipitation exceedance BSS is near neutral after week 1. Results support improved probabilistic skill and tail representation, but not calibrated operational rare-event probabilities. Case studies of the 2022 UK heatwave, 2023 California atmospheric rivers, and 2022 Pakistan floods illustrate the reach and limits of dynamically informed refinement.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.30159",
  "author_total": 2,
  "authors": [
    {
      "name": "Abdullah A. Fahad"
    },
    {
      "name": "Manmeet Singh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0hvxspf4f6x3t.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.30159",
  "fulltext_url": null,
  "id": "0hvxspf4f6x3t",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.30159",
  "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",
      "physics.ao-ph"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7223300733",
  "pdf_url": "https://arxiv.org/pdf/2610.30159",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0hvxspf4f6x3t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:34Z",
      "native_id": "2610.30159",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.30159"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Dynamical-Model-Informed Stochastic Flow Matching for Global Probabilistic Subseasonal Forecasting of Means and Extremes",
  "title_text": "Dynamical-Model-Informed Stochastic Flow Matching for Global Probabilistic Subseasonal Forecasting of Means and Extremes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:43Z",
  "url": "https://research.draconitic.com/paper/0hvxspf4f6x3t",
  "venue": "arXiv",
  "versions": []
}