Articles | Volume 18, issue 9
https://doi.org/10.5194/essd-18-6763-2026
© Author(s) 2026. 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-18-6763-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
14 Sep 2026
Data description article |
| 14 Sep 2026
| 14 Sep 2026
TPHH: a long-term (1901–2023) high-resolution (1∕30°) near-surface humidity dataset for the Tibetan Plateau generated via spatial downscaling based on hybrid-structure deep learning
Zheng Jin
College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
Data Science & Artificial Intelligence Research Institute, China Unicom, No. 21 Financial Street, Beijing 100013, China
Unicom Data Intelligence, China Unicom, No. 21 Financial Street, Beijing 100013, China
Qinglong You
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
Zhaoxiang Liu
CORRESPONDING AUTHOR
Data Science & Artificial Intelligence Research Institute, China Unicom, No. 21 Financial Street, Beijing 100013, China
Unicom Data Intelligence, China Unicom, No. 21 Financial Street, Beijing 100013, China
Jintao Zhang
Yunnan Key Laboratory of Plateau Geographical Process and Environmental Changes, Faculty of Geography, Yunnan Normal University, Kunming 650050, China
Huan Hu
Data Science & Artificial Intelligence Research Institute, China Unicom, No. 21 Financial Street, Beijing 100013, China
Unicom Data Intelligence, China Unicom, No. 21 Financial Street, Beijing 100013, China
Ping Chen
Data Science & Artificial Intelligence Research Institute, China Unicom, No. 21 Financial Street, Beijing 100013, China
Unicom Data Intelligence, China Unicom, No. 21 Financial Street, Beijing 100013, China
Xiang Liu
Data Science & Artificial Intelligence Research Institute, China Unicom, No. 21 Financial Street, Beijing 100013, China
Unicom Data Intelligence, China Unicom, No. 21 Financial Street, Beijing 100013, China
Zipeng Wang
Data Science & Artificial Intelligence Research Institute, China Unicom, No. 21 Financial Street, Beijing 100013, China
Unicom Data Intelligence, China Unicom, No. 21 Financial Street, Beijing 100013, China
Kai Wang
Data Science & Artificial Intelligence Research Institute, China Unicom, No. 21 Financial Street, Beijing 100013, China
Unicom Data Intelligence, China Unicom, No. 21 Financial Street, Beijing 100013, China
Shiguo Lian
CORRESPONDING AUTHOR
Data Science & Artificial Intelligence Research Institute, China Unicom, No. 21 Financial Street, Beijing 100013, China
Unicom Data Intelligence, China Unicom, No. 21 Financial Street, Beijing 100013, China
Shichang Kang
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China
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Short summary
This study presents the TPHH (Tibetan Plateau Historical High) dataset, a high-resolution (1/30°) monthly climate dataset for the Tibetan Plateau spanning 1901–2023, featuring 2 m temperature, specific humidity, and surface pressure. By employing a hybrid deep learning framework (FourCastNet), we downscaled coarse historical data through the synergistic mapping of total-field signals and terrain constraints. Validated against independent observations, this physically-consistent dataset bridges the pre-satellite data gap.
This study presents the TPHH (Tibetan Plateau Historical High) dataset, a high-resolution...
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