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The Tibetan Plateau acts as the “Asian Water Tower” and faces regional amplified warming compared to the global climate change baseline. Given the Tibetan 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 currently available high-resolution datasets only extend back to the 1980s, while records extending into the pre-satellite era remain 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 Climatic Research Unit Time-Series, CRU_TS) and short-range high-resolution fields (1/30° × 1/30° covering 1979–2023 from the Tibetan Plateau Multi-source Meteorological Forcing Dataset; TPMFD) via spatial downscaling. The reconstructed fields were evaluated using target-referenced performance metrics, inter-product comparisons, and station-based consistency assessments. FourCastNet provides a data-driven statistical mapping from coarse CRU_TS predictors to high-resolution TPMFD target fields and reproduces learned terrain-related spatial gradients without explicitly enforcing physical equations or terrain constraints. The pre-1950 reconstruction remains less observationally constrained, and fine-scale early-century variability should be interpreted cautiously. Open access to this dataset is at https://doi.org/10.57760/sciencedb.36169 (Chen, 2026).
