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<p>This Data Descriptor provides a comprehensive global dataset of bioclimatic and agrometeorological indicators intended for use in ecological, environmental, and agricultural research. The dataset is derived exclusively from the AgERA5 v2.0 reanalysis product, ensuring a consistent and high-quality methodological foundation. We provide over 70 indicators and statistics with global spatial coverage at a resolution of 0.1° for the years 1979 to 2023. Variables are available at annual and, where applicable, monthly temporal resolutions to support analyses that require information on seasonal dynamics. The dataset includes 19 bioclimatic indicators following established definitions, a set of 16 specialized agrometeorological indices, and 40 variables covering statistical variables (mean, minimum, maximum, and variance) for all foundational climate variables. By offering this comprehensive collection of variables from a single input source, we reduce the technical barriers for applied sciences requiring climate data.</p>

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A global dataset of climate indices and statistics for agro-environmental research covering 1979 to 2023

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

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  • Kevin Schneider,
  • Ana Klinnert

摘要

This Data Descriptor provides a comprehensive global dataset of bioclimatic and agrometeorological indicators intended for use in ecological, environmental, and agricultural research. The dataset is derived exclusively from the AgERA5 v2.0 reanalysis product, ensuring a consistent and high-quality methodological foundation. We provide over 70 indicators and statistics with global spatial coverage at a resolution of 0.1° for the years 1979 to 2023. Variables are available at annual and, where applicable, monthly temporal resolutions to support analyses that require information on seasonal dynamics. The dataset includes 19 bioclimatic indicators following established definitions, a set of 16 specialized agrometeorological indices, and 40 variables covering statistical variables (mean, minimum, maximum, and variance) for all foundational climate variables. By offering this comprehensive collection of variables from a single input source, we reduce the technical barriers for applied sciences requiring climate data.