{
  "abstract": "Medium-range precipitation forecasts are impaired by persistent systematic biases, lead-time-dependent error accumulation, and coarse spatial resolution, restricting their reliability for flood-drought risk assessment. Existing AI correction techniques lack dedicated modeling for multi-day dynamic bias evolution and proper meteorological constraints, often generating over-smoothed rainfall structures, and cannot meet operational deployment demands. This work introduces PCSDiff, a cascaded task-decoupled diffusion framework targeting 10-day precipitation bias correction and downscaling. To jointly counteract temporal error drifts and reconstruct physically plausible local precipitation details, PCSDiff integrates the Precipitation Intensity-aware Multi-branch Decoder (PIMD) module for dynamic multi-day error mitigation using synoptic-temporal features, followed by a two-phase conditional diffusion super-resolution module to restore fine-scale precipitation patterns. Evaluated against CMA-CRA observations over China after global-data training, PCSDiff cuts RMSE by 16.1% and lifts ACC by 13.9% relative to raw ECMWF forecasts at 3-10-day lead times, and consistently outperforms mainstream deep-learning baselines on both general and extreme-precipitation metrics. Benefiting from a streaming inference pipeline, our method achieves low-latency rolling forecasting for practical meteorological operations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.06942",
  "author_total": 10,
  "authors": [
    {
      "name": "Yuze Sun"
    },
    {
      "name": "Shiyi Wang"
    },
    {
      "name": "Jiancheng Pan"
    },
    {
      "name": "Die Wang"
    },
    {
      "name": "Andreas F. Prein"
    },
    {
      "name": "Wentao Luo"
    },
    {
      "name": "Linhan Jiang"
    },
    {
      "name": "Jie Wu"
    },
    {
      "name": "Quan Zhang"
    },
    {
      "name": "Xiaomeng Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cqjj2sn2eyabs.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.06942",
  "fulltext_url": null,
  "id": "cqjj2sn2eyabs",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.06942",
  "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": "W7212065269",
  "pdf_url": "https://arxiv.org/pdf/2609.06942",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cqjj2sn2eyabs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "2609.06942",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.06942"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast",
  "title_text": "PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/cqjj2sn2eyabs",
  "venue": "arXiv",
  "versions": []
}