{
  "abstract": "Accurate high-resolution wind field simulations are critical for resolving fine-scale atmospheric dynamics, yet the simulation of wind fields in Earth System Models (ESMs) remains limited by coarse spatial resolution and systematic biases. To address this, data-driven down scaling techniques have been widely used to enhance coarse-resolution ESM outputs. However, existing methods are typically tied to fixed discretizations, limiting generalization across models with different native resolutions. Here we formulate global near-surface wind downscaling as an operator-learning problem on continuous atmospheric state fields and develop a downscaling neural operator that maps coarse-scale fields to fine-scale counterparts across heterogeneous discretizations. The operator learning-based neural downscaling framework outperforms dominant baselines, recovers fine-scale physical structures, and generalizes to previously unseen ESMs and future climate scenarios without retraining, while preserving long-term wind projection trends. These findings establish a generalizable paradigm for high-resolution climate downscaling across diverse simulation outputs and future scenarios.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.03757",
  "author_total": 6,
  "authors": [
    {
      "name": "Chenxi Yu"
    },
    {
      "name": "Jianan Wei"
    },
    {
      "name": "Hanlin Kong"
    },
    {
      "name": "Hao Sun"
    },
    {
      "name": "Bian He"
    },
    {
      "name": "Wenguan Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/86nsfx17vzdsx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.03757",
  "fulltext_url": null,
  "id": "86nsfx17vzdsx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.03757",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7220666241",
  "pdf_url": "https://arxiv.org/pdf/2610.03757",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/86nsfx17vzdsx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.03757",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.03757"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Generalizable Neural Downscaling of Earth System Model Wind Fields via Continuous Dynamics Modeling",
  "title_text": "Generalizable Neural Downscaling of Earth System Model Wind Fields via Continuous Dynamics Modeling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/86nsfx17vzdsx",
  "venue": "arXiv",
  "versions": []
}