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 Zhong Jin, Zezhou Chen, Qinglong You, Zhaoxiang Liu, Jintao Zhang, Huan Hu, Ping Chen, Xiang Liu, Zipeng Wang, Kai Wang, Shiguo Lian, Shichang Kang

Earth system science data · 14 September 2026 · Fudan University (FuXi)

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In one sentence. FourCastNet-based hybrid deep learning downscales 1901–2023 CRU fields to 1/30° humidity, temperature and pressure over the Tibetan Plateau, validated against in-situ sites.

Abstract

Abstract. The Tibetan Plateau acts as the "Asian Water Tower" and faces regional amplified warming compared to the global climate change baseline. Given the Tibet Plateau’s pronounced alpine terrain, i.e., significant elevation gradients within short horizontal distances, studies on climate changes/dynamics over this mountainous region fundamentally depend on spatially high-resolution datasets. However, most of currently available spatially high-resolution datasets only extend back to the 1980s, with prolonged temporal coverage data of pre-satellite era remaining scarce, especially for near surface atmospheric humidity. Thus, our study implements a hybrid-structure-based deep learning framework to generate monthly 2 m specific humidity, 2 m temperature and surface pressure at 1/30° × 1/30° horizontal resolution during 1901–2023. Briefly, employing a hybrid-structure model (FourCastNet by NVIDIA®), historical high-resolution fields (1/30° × 1/30° covering 1901–2023) are generated based on long-range low-resolution (0.5° × 0.5° covering 1901–2023 from CRU) and short-range high-resolution fields (1/30° × 1/30° covering 1978–2023 from TPMFD) via spatial downscaling. The produced data were validated against multiple related datasets, with independent in-situ site observations serving as the reference, and showed superior performance compared to most of them. Our study demonstrates that in topographically complex regions like the Tibetan Plateau, where meteorological fields exhibit strong physical dependencies on terrain, the synergistic mapping between total-field signals and subregional terrain constraints can be effectively achieved through hybrid-structure deep learning, thereby enabling this physically-consistent downscaling approach. Open access to this dataset is at https://doi.org/10.57760/sciencedb.36169.

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