High-quality precipitation data are vital for hydrological research. In regions with sparse observation stations, reliable gridded data cannot be obtained through interpolation, while the coarse resolution of satellite products fails to meet the demands of small watershed studies. Downscaling satellite-based precipitation products offers an effective solution for generating high-resolution data in such areas. Among these techniques, machine learning plays a pivotal role, with performance varying according to surface conditions and algorithmic mechanisms. Using the Qinghai Lake Basin as a case study and rain gauge observations as reference data, this research conducted a systematic comparative evaluation of nine machine learning algorithms (ANN, CLSTM, GAN, KNN, MSRLapN, RF, SVM, Transformer, and XGBoost) for downscaling IMERG precipitation products from 0.1° to 0.01° resolution. The primary objective was to identify the optimal downscaling method for the Qinghai Lake Basin by assessing spatial accuracy, seasonal performance, and residual sensitivity. Seven metrics were employed for assessment: correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), standard deviation ratio (Sigma Ratio), Kling-Gupta Efficiency (KGE), and bias. On the annual scale, KNN delivered the best overall results (KGE = 0.70, RMSE = 17.09 mm, Bias = −3.31 mm), followed by Transformer (KGE = 0.69, RMSE = 17.20 mm, Bias = −3.24 mm). During the cold season, KNN and ANN both performed well (KGE = 0.63; RMSE = 5.97 mm and 6.09 mm; Bias = −1.76 mm and −1.75 mm), with SVM ranking next (KGE = 0.63, RMSE = 6.11 mm, Bias = −1.63 mm). In the warm season, Transformer yielded the best results (KGE = 0.74, RMSE = 23.35 mm, Bias = −1.03 mm), followed closely by ANN and KNN (KGE = 0.74; RMSE = 23.38 mm and 23.57 mm; Bias = −1.08 mm and −1.03 mm, respectively). GAN consistently underperformed across all temporal scales, with annual, cold-season, and warm-season KGE values of 0.61, 0.43, and 0.68, respectively—worse than the original 0.1° IMERG product. Considering the ability to represent spatial precipitation gradients, KNN emerged as the most suitable method for IMERG downscaling in the Qinghai Lake Basin. Residual analysis revealed error concentrations along the lakeshore, and model performance declined when residuals exceeded specific thresholds—highlighting the need to account for model-specific sensitivity during correction. SHAP analysis based on ANN, KNN, SVM, and Transformer identified NDVI (0.218), longitude (0.214), and latitude (0.208) as the three most influential predictors. While longitude and latitude affect vapor transport by representing land–sea positioning, NDVI is heavily influenced by anthropogenic activities and sandy surfaces in lakeshore regions, thus limiting prediction accuracy in these areas. This work delivers a high-resolution (0.01°) precipitation dataset for the Qinghai Lake Basin and provides a practical basis for selecting suitable downscaling methods in similar environments.

The Qinghai–Tibet Plateau is a globally significant ecological barrier, and its ecological water requirement (EWR) is of great importance for the stability of regional ecosystems and water resource management. Based on meteorological data, this study employed the Penman–Monteith method, combined with the vegetation coefficient ( K _c ) and soil moisture limitation coefficient ( K _s ), to estimate the EWR and ecological water deficit (EWD) for the entire Qinghai–Tibet Plateau and its 11 climate zones from 1991 to 2020. The spatiotemporal variation patterns were also analyzed. The results indicate: (1) Among the seven major vegetation types, low cover grassland has the highest EWR (7.28 × 10 ¹⁰ m³), while other forest land has the lowest (0.04 × 10 ¹⁰ m³). (2) In the wet year (2014), normal water year (2016), and dry year (2012), vegetation growth across the Qinghai–Tibet Plateau generally shows a water surplus, with the wet year having a greater surplus than the dry year. (3) Except for the Qaidam region (HD1) and the northern Tibet region (HD2), the other nine climate zones show a water surplus. (4) The peak EWR for different zones and vegetation types occurs during the mid‐growth period. The most severe water deficit in HD1 and HD2 regions occurs during the early growth stage, with precipitation addressing 12.59% and 26.08% of the deficit, respectively. This study provides a scientific basis for water resource management on the Qinghai–Tibet Plateau.