the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A 1-km daily high-accuracy meteorological dataset of air temperature, atmospheric pressure, relative humidity, and sunshine duration across China (1961–2021)
Abstract. Fine-resolution and high-accuracy meteorological datasets are essential for understanding climate change processes and their cascading impacts on hydrology, water resources management, and ecological systems. In this study, we present a nationwide, high-resolution dataset of six daily meteorological variables across China from 1961 to 2021, including average temperature, maximum temperature, minimum temperature, atmospheric pressure, relative humidity, and sunshine duration. The dataset was generated through a hierarchical reconstruction framework that utilizes daily observations from 2345 meteorological stations across China, combined with station-level topographic attributes (latitude, longitude, and elevation). By decoding the nonlinear relationships among six meteorological variables and their spatial covariates, the framework enables the generation of gridded daily fields at 1 km resolution with spatial continuity and internal consistency. Validation against 118 in-situ stations confirms that the dataset achieves high accuracy across all variables, with average, maximum, and minimum temperatures exhibiting minimal errors (median RMSEs: 1.03 °C, 1.19 °C, 1.34 °C; median MEs: -0.09 °C, -0.10 °C, -0.08 °C) and high consistency with in-situ data (median CCs: 1.00, 0.99, 0.99). Atmospheric pressure shows minimal error (median RMSE: 2.48 hPa; median ME: -0.02 hPa) and high consistency (median CC: 0.98). Although relative humidity has slightly weaker accuracy (median RMSE: 6.02 %; median ME: -0.5 %; median CC: 0.90), it still surpasses standard benchmarks. Sunshine duration maintains high precision (median RMSE: 1.48 h; median ME: 0.05 h; median CC: 0.93), demonstrating overall excellent product quality. Further comparison reveals that in high-altitude and topographically complex regions, the reconstructed product demonstrates higher actual accuracy than suggested by station-to-grid validation, as spatial mismatches between stations and grid cells lead to systematic underestimation. Free access to the dataset available at https://doi.org/10.11888/Atmos.tpdc.301341 or https://cstr.cn/18406.11.Atmos.tpdc.301341.
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Status: open (until 27 Aug 2025)
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RC1: 'Comment on essd-2025-291', Anonymous Referee #1, 04 Jul 2025
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- This paper develops a long sequence dataset, and the research purpose and user target population of this dataset need to be further clarified;
- “The objective of this study is to develop a high-resolution and accuracy-assessed dataset of daily near-surface meteorological variables across mainland China, suitable for applications in hydrological modeling, environmental monitoring, and climate analysis.” How did the author consider the issue of temporal "homogeneity" in a long series dataset used for climate analysis?
- What is the quality of the raw observation data used to establish 1km grid data? Has the author conducted data quality evaluation, analysis, quality control, homogenization processing, etc. on the original observation data during its use?
- Compared to a grid spatial resolution of 1km * 1km, using over 2000 observation data from China is relatively insufficient, especially in the sparse observation areas of western China. How does the author consider this issue?
- How is the " day boundary issues" handled? The ground meteorological observation in China adopts "20:00 Beijing time" as the boundary point of the day, which means that the observation day is from 20:00 to 20:00 the next day. This standard is applicable to daily value statistics of factors such as precipitation and temperature. Prior to the 1980s, some stations had a phenomenon of inconsistent day boundaries (such as a few stations using 08 or local time), which led to a decrease in comparability between early data and other stations.
- Overall evaluation: This long sequence dataset did not take into account the quality of the observation data used, day boundary issues, uniformity issues, etc. during the development process. Therefore, the dataset reconstructed in this article also has day boundary and uniformity issues, which will have a serious impact on downstream user research.
Citation: https://doi.org/10.5194/essd-2025-291-RC1 -
AC1: 'Reply on RC1', keke zhao, 07 Jul 2025
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We thank the reviewer for their valuable comments. The attached file contains our point-by-point responses.
Data sets
China's 1km daily reconstructed product of six meteorological elements (1961–2021) Keke Zhao, Denghua Yan, Tianling Qin, Chenhao Li, Dingzhi Peng, Yifan Song https://doi.org/10.11888/Atmos.tpdc.301341
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