Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-3987-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-3987-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
19 Aug 2025
Data description paper |
| 19 Aug 2025
| 19 Aug 2025
An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates
Jinlong Hu
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jiajia Su
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Qi Zhang
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jiaojiao Gou
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Department of Geographic Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
Qiaohong Sun
State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Short summary
We developed a high-precision daily precipitation dataset for the Chinese mainland called CHM_PRE V2. Using data from 3746 rain gauges, 11 precipitation-related variables, and advanced machine learning methods, we created a daily precipitation dataset spanning 1960–2023 with unprecedented accuracy. Compared to existing datasets, it better captures rainfall events while reducing false alarms. This work provides a reliable tool for studying water resources, climate change, and disaster management.
We developed a high-precision daily precipitation dataset for the Chinese mainland called...
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