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<p>High-resolution near-surface air temperature (SAT) datasets are essential for evaluating long-term SAT changes and developing management strategies in the Yangtze River Basin (YRB). However, existing SAT datasets have limited temporal coverage or coarser spatial resolution. In this study, a summer SAT dataset for 850‒2005 at a spatial resolution of 1° × 1° was developed by integrating the Millennium Global Climate Model (GCM) simulations from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) with four proxy datasets. A weighted ensemble of multi- GCM simulations was created, and bias-correction was performed using an updated cumulative distribution function (CDF) method to improve gridded summer SAT dataset. The bias-corrected dataset was subsequently integrated with four gridded paleo summer SAT datasets covering the past millennium using a grid-weighted averaging technique based on an entropy method to improve low-frequency signal robustness. Spatiotemporal evaluation metrics showed substantial improvements in corrected summer SAT compared with raw GCMs. The integrated dataset demonstrates robust performance in revealing the spatiotemporal changes in SAT in the YRB during the past millennium.</p>

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Gridded millennial summer temperature dataset over the Yangtze River Basin

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Adil Dilawar,
  • Jianping Duan,
  • Yawen Liu,
  • Iman Babaeian,
  • Seyed Asaad Hosseini

摘要

High-resolution near-surface air temperature (SAT) datasets are essential for evaluating long-term SAT changes and developing management strategies in the Yangtze River Basin (YRB). However, existing SAT datasets have limited temporal coverage or coarser spatial resolution. In this study, a summer SAT dataset for 850‒2005 at a spatial resolution of 1° × 1° was developed by integrating the Millennium Global Climate Model (GCM) simulations from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) with four proxy datasets. A weighted ensemble of multi- GCM simulations was created, and bias-correction was performed using an updated cumulative distribution function (CDF) method to improve gridded summer SAT dataset. The bias-corrected dataset was subsequently integrated with four gridded paleo summer SAT datasets covering the past millennium using a grid-weighted averaging technique based on an entropy method to improve low-frequency signal robustness. Spatiotemporal evaluation metrics showed substantial improvements in corrected summer SAT compared with raw GCMs. The integrated dataset demonstrates robust performance in revealing the spatiotemporal changes in SAT in the YRB during the past millennium.