{
  "abstract": "Climate comfort constitutes a pivotal metric for assessing the influence of climate change on human leisure and tourism pursuits, while existing studies on climate comfort face notable limitations. First, the predominant approach relies on indices that incorporate only single meteorological variables, thereby restricting their capacity to encapsulate the holistic human perception of the climatic milieu. Second, there is a dearth of long-term (exceeding 30 years) and high-spatial-resolution comprehensive climate comfort indices, impeding thorough examinations of long-term climatic variability. Consequently, this study endeavors to develop a long-term, high-spatial-resolution comprehensive climate comfort index tailored to China. Utilizing monthly gridded meteorological data at a resolution of 0.25° × 0.25° spanning from 1961 to 2022, the temperature-humidity index, wind effect index, and clothing index were computed. Three weighting methodologies—namely, the expert scoring approach, the entropy weight method, and the Bayesian three-cornered hat method—were employed to generate three distinct sets of comprehensive climate comfort indices. Following this, uncertainties were quantified via the Bayesian three-cornered hat method, and the three indices were synthesized into a definitive composite climate comfort index. The ensuing dataset provides the high-resolution, long-term comprehensive climate comfort index for China, encompassing over six decades. It delineates spatial heterogeneity and seasonal dynamics, offering data for evaluating the ramifications of climate change on human leisure and tourism activities. The dataset is available at: https://doi.org/10.11888/Atmos.tpdc.303390.",
  "abstract_shown": true,
  "affiliations": [
    "Fujian Provincial Key Laboratory for Subtropical Resources and Environment, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117 China",
    "School of Geography and Tourism, Henan Normal University, Xinxiang, 453007 China"
  ],
  "article_type": "data paper",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Qun Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Lan Lin"
    },
    {
      "affs": [
        1
      ],
      "name": "Yayan Lu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7f67twvnbxwdp.bib",
  "cited_by": 0,
  "doi": "10.1038/s41597-026-07880-2",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13740296/",
  "id": "7f67twvnbxwdp",
  "issns": [
    "2052-4463"
  ],
  "landing_url": "https://doi.org/10.1038/s41597-026-07880-2",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Data Descriptor"
    ],
    [
      "openalex",
      "2305|Urban Heat Island Mitigation"
    ]
  ],
  "openalex": "W7168366672",
  "pdf_url": "https://www.nature.com/articles/s41597-026-07880-2_reference.pdf",
  "pmcid": "PMC13740296",
  "pmid": "42457722",
  "published": "2026-07-15",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/7f67twvnbxwdp.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T12:21:34Z",
      "native_id": "PMC13740296",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41597-026-07880-2"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Urban Heat Island Mitigation",
      "subfield": "environmental-science"
    }
  ],
  "title": "A High-Resolution, Long-Term, Comprehensive Climate Comfort Index Dataset for China (1961–2022)",
  "title_text": "A High-Resolution, Long-Term, Comprehensive Climate Comfort Index Dataset for China (1961–2022)",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T12:21:34Z",
  "url": "https://research.draconitic.com/paper/7f67twvnbxwdp",
  "venue": "Scientific Data",
  "versions": []
}