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<p>High-resolution climate data are important for understanding the impacts of climate change on multiple sectors worldwide. In this study, based on the latest released meteorological records during 1991–2020 and the recently updated general circulation models (GCMs), we established a 30-year averaged 0.01° (≈1 km) dataset of 5 basic climate variables and 23 bioclimatic variables, using ANUSPLIN software, delta correction (DC) downscaling, and cubic spline resampling method. Each variable contained monthly gridded historical data during 1991–2020 and bias-corrected future data over three periods (2021–2040, 2041–2070, 2071–2100), three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and 10 GCMs (including an ensemble model). The historical interpolations&#xa0;generated by the ANUSPLIIN software showed a&#xa0;good fit&#xa0;(above 0.91) with observations. The DC correction improved the accuracy of most GCM original simulations, reducing the bias by 0.69%–58.63%. This new dataset therefore demonstrates reliable data quality, and further provides high-resolution and bias-corrected long-term averaged historical and future climate data across China for ecological and climate impact studies.</p>

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A 1 km monthly dataset of historical and future climate changes over China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月13日

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  • Xiaofei Hu,
  • Shaolin Shi,
  • Borui Zhou,
  • Jian Ni

摘要

High-resolution climate data are important for understanding the impacts of climate change on multiple sectors worldwide. In this study, based on the latest released meteorological records during 1991–2020 and the recently updated general circulation models (GCMs), we established a 30-year averaged 0.01° (≈1 km) dataset of 5 basic climate variables and 23 bioclimatic variables, using ANUSPLIN software, delta correction (DC) downscaling, and cubic spline resampling method. Each variable contained monthly gridded historical data during 1991–2020 and bias-corrected future data over three periods (2021–2040, 2041–2070, 2071–2100), three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and 10 GCMs (including an ensemble model). The historical interpolations generated by the ANUSPLIIN software showed a good fit (above 0.91) with observations. The DC correction improved the accuracy of most GCM original simulations, reducing the bias by 0.69%–58.63%. This new dataset therefore demonstrates reliable data quality, and further provides high-resolution and bias-corrected long-term averaged historical and future climate data across China for ecological and climate impact studies.