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<p>High-resolution air temperature (Ta) data are essential for environmental monitoring, public health evaluation, and urban climate adaptation, particularly in mountainous megacities with sharp spatial gradients. This study presents a gridded daily Ta dataset at 30 m resolution for the Chongqing Metropolitan Circle, China, spanning 2016 to 2024. This area features a unique topography of alternating ridge–valley corridors, creating strong microclimatic contrasts within densely populated urban areas. The dataset was generated using a Spatially Varying Coefficient Model with Sign Preservation (SVCM-SP) framework that integrates multi-year Landsat-derived land surface temperature, digital elevation, and observations from an average of 215 meteorological stations per year, with an average inter-station distance of 37.7 km. Validation at both daily and monthly scales confirms high spatial and temporal consistency across complex terrain and seasonal conditions. The dataset provides fine-scale daily maximum and minimum temperature estimates and supports diverse applications such as heatwave risk assessment, urban climate research, and adaptation policy design in rapidly urbanizing mountainous regions.</p>

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Daily air temperature estimates at 30-meter resolution in the Chongqing Metropolitan Area, China, 2016–2024

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月22日

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  • Wan Zhou,
  • Shiqi Yang,
  • Hao Zhu,
  • Zhiqiang Liu,
  • Meiyan Zhao,
  • Yuyu Zhou

摘要

High-resolution air temperature (Ta) data are essential for environmental monitoring, public health evaluation, and urban climate adaptation, particularly in mountainous megacities with sharp spatial gradients. This study presents a gridded daily Ta dataset at 30 m resolution for the Chongqing Metropolitan Circle, China, spanning 2016 to 2024. This area features a unique topography of alternating ridge–valley corridors, creating strong microclimatic contrasts within densely populated urban areas. The dataset was generated using a Spatially Varying Coefficient Model with Sign Preservation (SVCM-SP) framework that integrates multi-year Landsat-derived land surface temperature, digital elevation, and observations from an average of 215 meteorological stations per year, with an average inter-station distance of 37.7 km. Validation at both daily and monthly scales confirms high spatial and temporal consistency across complex terrain and seasonal conditions. The dataset provides fine-scale daily maximum and minimum temperature estimates and supports diverse applications such as heatwave risk assessment, urban climate research, and adaptation policy design in rapidly urbanizing mountainous regions.