The variables used in this study were sourced from the CRU_TS_4.08 dataset (Harris et al., 2020), a widely used global gridded climate dataset produced by the Climatic Research Unit (CRU) at the University of East Anglia. The generation process for this dataset is both complex and rigorous, with its foundation being the monthly observations from thousands of meteorological stations worldwide. Its core construction methodology is analogous to an “anomaly” or “delta” method, involving the following steps:

Calculation of station anomalies: Initially, observational series from over 5000 weather stations globally are utilized. The monthly observational data from each station is converted into an anomaly series by comparing it against the station's monthly mean for the 1961–1990 standard period. Anomalies for temperature-based variables are calculated by subtraction, while precipitation anomalies are calculated by division.

The TPMFD dataset was used as the target dataset for model training and target-referenced performance assessment. It is a high-resolution ground meteorological forcing dataset developed specifically for the Tibetan Plateau, with a temporal coverage from 1979–2023 and a spatial resolution as high as 1/30°. Its high spatial resolution is produced through multi-source fusion: 2 m temperature, 2 m specific humidity, surface pressure, and wind speed are derived from the fusion of short-term high-resolution WRF simulations, long-term ERA5 data, and dense ground station observations (Jiang et al., 2025). This fusion combines dynamical-model output, reanalysis fields, and station-based corrections, while retaining uncertainties inherited from each source. TPMFD is therefore used as the high-resolution target dataset rather than as error-free ground truth. We use its 2 m temperature (temp), 2 m specific humidity (shum), and surface pressure (pres) fields for 1979–2023. TPMFD is described and accessed through Jiang et al. (2025) and its corresponding repository: 10.11888/atmos.tpdc.300398 (Yang et al., 2023a).

For station-based consistency evaluation, we compiled monthly 2 m temperature, surface pressure, and relative-humidity records from 135 China Meteorological Administration (CMA) stations. The temperature and relative-humidity series were quality controlled and homogenized following Cao et al. (2016) and Zhu et al. (2015), respectively. Because CMA observations contributed to TPMFD production, possible information overlap cannot be excluded. The spatial distribution of the collected sites within the Tibetan Plateau is shown in Fig. 1.

Of the 135 CMA stations, 125 have observations extending to the pre-1979 period, whereas the remaining 10 have observations only from 1979 onward. For the pre-1979 records, 125 stations contained valid data, yielding 28 800 site-month records for temperature, 25 570 for pressure, 28 077 for relative humidity, and 25 422 for specific humidity. For the period of 1979–2023, 135 stations provided 68 840 records for temperature, 70 411 for pressure, 68 714 for relative humidity, and 68 661 for specific humidity.

To compare selected statistical characteristics of TPHH with existing products, we considered reanalysis datasets, gridded observations, and climate-model simulations.

In addition to ERA5-Land (1950–present) (Muñoz-Sabater et al., 2021) and 20CRv3 (1836–2015) (Slivinski et al., 2019), we included two widely used reanalysis datasets: the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q, 1948–present) (Harada et al., 2026) and the NCEP/NCAR Reanalysis 1 (NCEP-R1, 1948–present) (Kalnay et al., 1996). For gridded observations, we utilized the UDel (University of Delaware) air temperature and precipitation dataset (V5.01, 1900–2017) (Willmott, 2000). For downscaling-based reconstructions, we utilized the 1 km monthly temperature (1901–2024) developed by Peng et al. (2019). Furthermore, to assess the model's performance against traditional climate models, we selected historical simulations (covering 1850–2014) from six GCMs participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) (O'Neill et al., 2016): CESM2, UKESM1-0-LL, MPI-ESMI-2-HR, IPSL-CM6A-LR, BCC-CSM2-MR, and CanESM5. All CMIP6 data were bilinearly interpolated to the target resolution for consistent comparison.

An efficient downscaling model is key to bridging the resolution gap. In this study, we employ FourCastNet as the core downscaling model for handling the complex spatiotemporal physical fields over the TP. As a data-driven weather model, FourCastNet is one of the state-of-the-art deep learning models originally designed for global machine-learning-based weather forecasting. Its architecture combines the strengths of computer vision and Fourier neural operators, making it particularly suitable for the climate data downscaling task in our study. Its core advantage lies in its ability to efficiently learn multi-scale spatial features and total-field dependencies, which is crucial for downscaling climate fields from coarse CRU gridded data over the complex terrain of the TP. The production configuration, software, and hardware details are provided in Table S1 in the Supplement.

The hybrid-structure of the model is primarily composed of two key components: a Vision Transformer (ViT) backbone and the Adaptive Fourier Neural Operator (AFNO) serving as the core mixing layer.

The workflow of our approach is visually displayed in Fig. 3, showing the overall workflow from data preprocessing to final evaluation. As shown in Fig. 3, the reconstruction pipeline is composed of four successive steps:

Domain of input and optimization: The information used is dynamic climate maps from CRU. Next, following a bilinear interpolation, the coarse climate fields are concatenated to form the multi-channel input tensor for the deep learning model.

To evaluate the quality of the reconstruction model quantitatively, we compared reconstructed fields with the TPMFD target fields during 1979–2023 using a suite of common statistical metrics, namely coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the Index of Agreement (IOA).

In the following equations, Si denotes the simulated value at time step i, Oi denotes the target TPMFD value, and S‾ and O‾ denote the mean of the simulated and target values, respectively. The number of all samples is n.

Coefficient of determination (R2): This measure indicates how much variance in the observed data can be accounted for by the model. It shows how strongly linear simulated and targeted values are related. Larger values of R2 indicate better model fits. Its value lies between 0 and 1, and 1 denotes a perfect correlation.1R2=∑i=1n(Si-S‾)(Oi-O‾)∑i=1n(Si-S‾)2∑i=1n(Oi-O‾)22

In addition to the primary accuracy metrics, a series of graphical and comparative analyses were conducted to provide complementary reconstruction-performance, station-consistency, breakpoint, trend, DEM-sensitivity, and proxy-window diagnostics. These diagnostics assess different aspects of consistency and plausibility but do not constitute a comprehensive uncertainty estimate. The roles and limitations of each diagnostic are described below.

Density scatter plots: To evaluate the validity of point-to-point matching and error distribution (1979–2023) for 2 m temperature (temp), 2 m specific humidity (shum), and surface pressure (pres), density scatter plots were used. The first shows the predictions of the nine individual models produced by nine-fold cross-validation against the corresponding TPMFD target values. The plots reveal systematic departures, scatter, and the concentration of samples around the 1 : 1 line.

Second, to place our model's performance in the context of other widely-used climate datasets, a series of Taylor diagrams were produced using multiple gridded data products as references. These comparisons describe how TPHH reproduces selected statistical properties relative to different products. Agreement among products is interpreted as inter-product consistency rather than independent validation. The specific comparisons were as follows: temp was compared against 20CRv3, ERA5, and CRU_TS_4.08; shum was compared against 20CRv3 and ERA5; pres was compared against 20CRv3 and ERA5.

To examine proxy-based temporal plausibility, we fitted a standardized ternary regression model to 41 tree-ring chronology groups. The model combined JJA temperature, a candidate moisture window (AMJ, MJJ, or JJA specific humidity), and a lag-1 tree-ring term. Prior to modeling, the TPHH summer temperature (TJJA), the early-summer specific humidity (QMJJ), and the tree-ring width index (TRWI) (Fang et al., 2022), were standardized using Z-score normalization to eliminate dimensional discrepancies. To represent contemporaneous climatic covariates together with serial persistence in the tree-ring chronology, the current-year thermal and moisture variables were combined with a first-order autoregressive term (the lag-1 TRWIt-1). The normalized ternary model is expressed as: 5TRWIt=β0+β1×TJJA+β2×QMJJ+β3×TRWIt-1

This standardized modeling effectively estimates the interannual theoretical tree growth driven by the TPHH historical climate. An 11-year moving average was shown only to visualize low-frequency co-variation.

To assess the ability of the hybrid-structure deep-learning model (FourCastNet) to reconstruct high-resolution climate fields from CRU predictors, we performed a target-referenced performance assessment of its output using the TPMFD dataset during 1979–2023 as the target dataset.

Having demonstrated the model's calibration performance, we now turn to performance validation of reconstruction. We employed two complementary comparison strategies: (1) station-based consistency evaluation using CMA records, and (2) inter-product statistical comparison. Neither strategy is interpreted as fully independent validation of early-period accuracy.

Having evaluated selected aspects of reconstruction performance, station consistency, and temporal plausibility, this section examines long-term spatial and temporal characteristics of TPHH.

The fitting result demonstrates a high temporal consistency with the annual tree-ring proxy series. As illustrated in Fig. 12 for a representative chronology, the smoothed climate-driven fitted series aligns tightly with the actual proxy record, yielding a Pearson correlation coefficient exceeding 0.8. Specifically, the lag-1 autoregressive term exhibits the largest standardized coefficient (β=0.37), highlighting the dominant role of physiological inertia. This indicates that radial growth on the TP is heavily subsidized by non-structural carbohydrates stored in the preceding year. Early-summer specific humidity emerges as the primary limiting factor (β=0.26), with a contribution nearly three times greater than that of peak growing-season temperature (β=0.05). This long-term coherence confirms that the TPHH dataset captures a part of the annual climate fluctuations. The spatial distribution and fitting results for totally 41 groups of proxy locations are detailed in Figs. S8–S23.

The downscaling framework is data driven. FourCastNet learns nonlinear mappings between upsampled CRU_TS predictors and high-resolution TPMFD target fields, including statistical gradients associated with terrain. Its architecture and MSE loss do not explicitly impose elevation, slope, aspect, the Clausius–Clapeyron relationship, the hypsometric equation, or conservation laws. In the DEM ablation experiment, adding an explicit DEM channel changed the spatially averaged R2 and IOA by less than 0.001 (Figs. S24–S29), indicating little additional predictive information under the tested configuration. Accordingly, TPHH patterns are described as terrain related and physically plausible, not physically or dynamically guaranteed.

Although the primary objective of TPHH is to reconstruct near-surface atmospheric humidity fields via machine-learning downscaling, this product also demonstrates advantages in air temperature reconstruction performance. Traditional reconstruction methods, such as the delta downscaling interpolation employed by Peng et al. (2019), rely heavily on the assumption of spatially continuous lapse rates. While effective in homogenous terrain, these methods treat the “terrain-climate” relationship as a static, local linear function of elevation. These frameworks are insufficient to reflect various terrain-atmosphere feedback conditions. In the complex hinterlands of the TP, where terrain governance induces various features, this method struggles to capture the high-frequency variability, leading to the “smoothing effect” observed in their results. Recent advancements by Yang et al. (2023b) introduced a deep-learning model based on generative adversarial network (GAN) to reconstruction. However, standard convolutional neural networks (CNNs) used in such studies typically operate with limited “receptive fields”. They excel at extracting local features but often fail to capture long-range dependencies, that is the critical “teleconnections” between the large-scale synoptic background and sub-regional micro-climates. As a result, while the GAN-based product improves local texture, it may lose physical consistency across the broader “total-field” when the input station data is sparse. Figure S7 shows that all three products have larger temperature RMSE in parts of complex terrain, with differing spatial patterns. TPHH has lower target-referenced RMSE than the two selected products in several high-relief regions, but this comparison against TPMFD does not establish physical consistency or identify the causal limitations of the competing methods. Due to its insufficient spatial resolution (∼10 km × 10 km), the GAN-based product even shows widespread noise in the extremely rugged southeastern TP, failing to follow the actual terrain patterns. Notably, the bias amplification of the delta-downscaling-based product in high-relief areas is entirely and substantially greater than that of TPHH. In the high-elevation central-southern TP (around 29° N, 93° E), TPHH even effectively mitigated the influence of mountainous terrain and did not display elevation-dependent bias, providing a stark contrast to the delta-downscaling-based product.

Despite the advancements in downscaling reconstruction, TPHH has certain limitations. First, the reconstruction quality is inherently bounded by the temporal consistency of the predictor dataset. Although CRU_TS provides long temporal coverage, any systematic biases in the early 20th-century observational records could propagate into the TPHH product. Second, while the 1/30° × 1/30° resolution is a major advancement, it may still be insufficient to resolve micro-climate features in extremely narrow valleys or glaciated peaks smaller than the grid scale. Future iterations of this work will aim to incorporate multi-source predictors (e.g., paleo-climate proxies) to further constrain uncertainties in the pre-1950 observational void. The post-1979 overlap period, the 1950–1978 historical period, and the observationally sparse pre-1950 period have different levels of constraint. FourCastNet transfers statistical relationships learned from modern CRU_TS–TPMFD pairs and cannot recover mesoscale variability absent from early CRU_TS predictors. Pettitt and trend diagnostics support regional-scale temporal consistency but do not quantify pre-1950 error or provide a comprehensive uncertainty product. CMA station comparisons may overstate independence because CMA observations contributed to TPMFD, and the western and central high-elevation Plateau remain weakly constrained by direct observations. Fine-scale early-century variability in these regions should therefore be interpreted cautiously.