A 1 km daily high-accuracy meteorological dataset of air temperature, atmospheric pressure, relative humidity, and sunshine duration across China (1961–2021)

作者：Keke Zhao, Denghua Yan, Qin Tianling, Chenhao Li, Dingzhi Peng, Yifan Song · 发表于：Earth system science data · 年份：2025 · DOI：10.5194/essd-17-7251-2025 · 被引用次数：5 · 研究领域：Remote Sensing in Agriculture、Meteorological Phenomena and Simulations、Precipitation Measurement and Analysis

Abstract. The lack of high-accuracy, fine-resolution meteorological datasets in China has hindered progress in climate, hydrological, and ecological studies. In this study, we present a 1 km daily dataset spanning 1961–2021 across China, which includes six key variables – average, maximum, and minimum temperature, atmospheric pressure, relative humidity, and sunshine duration – to provide a reliable foundation for advancing related research and applications. The dataset was generated using a novel hierarchical reconstruction framework that leveraged daily observations from 2345 meteorological stations and incorporated topographic attributes. This approach effectively decodes the nonlinear relationships between the meteorological variables and their spatial covariates, ensuring the generation of gridded daily fields that are both high-resolution and spatially continuous. Validation against 146 independent stations confirmed the high accuracy of the dataset. For average, maximum, and minimum temperatures, the errors are minimal (median root mean square errors (RMSEs): 1.16, 1.19, 1.29 °C; median mean errors (MEs): −0.04, −0.10, −0.01 °C), and the consistency with in-situ data is very high (median correlation coefficients (CCs): 0.99, 0.99, 0.99). Atmospheric pressure also shows very small errors (median RMSE: 2.65 hPa; median ME: −0.06 hPa) and strong correlation (median CC: 0.97). Relative humidity exhibits relatively lower accuracy (median RMSE: 6.33 %; median ME: −0.52 %; me...