Sun Mengqi, Meng Qingyan, Zhang Linlin, Hu Xinli, Lei Xuewen, Chen Shize, Hou Junyan
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Data. 2025 May 6;12(1):749. doi: 10.1038/s41597-025-05032-6.
Global warming and urbanization serve as critical research themes in fine-scale climate studies, particularly in developed cities. This study aims to provide a high spatiotemporal resolution dataset of near-surface air temperatures for densely developed urban areas. The dataset comprises daily maximum, minimum, and mean temperatures for the summer months (June to August) from 2019 to 2023, at a spatial resolution of 100 m, across the Jiangbei climate zone in China. We applied the Convolutional Long Short-Term Memory (ConvLSTM) deep learning model with multi-source data, including ERA5 temperature data, topography, landcover and vegetation fraction cover. Model evaluation indicates high accuracy, with mean absolute errors (MAE) ranging from 0.564 to 0.784 °C, root mean square errors (RMSE) from 0.733 to 1.027 °C, and coefficients of determination (R) from 0.892 to 0.943. Our dataset is distinguished by the 100 m spatial resolution and the inclusion of recent summer data from 2023 at a daily scale. This work is valuable for urban or inner-urban climate studies on heatwave mitigation policies and adaptation strategies.
全球变暖和城市化是精细尺度气候研究中的关键主题，尤其是在发达城市。本研究旨在为高密度发展的城市地区提供一个高时空分辨率的近地表气温数据集。该数据集包含2019年至2023年夏季月份（6月至8月）的日最高、最低和平均气温，空间分辨率为100米，覆盖中国江北气候区。我们应用了卷积长短期记忆（ConvLSTM）深度学习模型，并结合了多源数据，包括ERA5温度数据、地形、土地覆盖和植被覆盖分数。模型评估表明精度较高，平均绝对误差（MAE）在0.564至0.784°C之间，均方根误差（RMSE）在0.733至1.027°C之间，决定系数（R）在0.892至0.943之间。我们的数据集以100米的空间分辨率和包含2023年最新的夏季日尺度数据为特色。这项工作对于城市或城市内部气候研究中关于热浪缓解政策和适应策略具有重要价值。
