Precipitation estimation over the Tibetan Plateau is a critical but challenging task due to sparse gauges and high altitudes. Traditional statistic methods are often insufficient to characterize the nonlinear relationship between different precipitation information, while machine learning techniques, particularly deep learning algorithms, offer a novel and powerful approach to improve the merging accuracy of multi‐source precipitation data by efficiently capturing their spatiotemporal dynamics features. This study introduced a novel strategy called Double Machine Learning (DML), which integrates meteorological information, satellite retrievals, and reanalysis data to produce a high‐precision multi‐source merging precipitation product at 0.1° × 0.1°, daily resolution for the Tibetan Plateau. The quantitative evaluation of DML was accomplished using both auto‐meteorological gauges and independent observations. Statistical scores indicate that the new DML‐based merging product apparently outperforms three widely‐used precipitation datasets (IMERG‐Final, GSMaP‐Gauge and ERA5) over the Tibetan Plateau. The proposed DML strategy effectively integrates the advantages of traditional machine learning and deep learning, significantly enhancing the algorithmic robustness and merging accuracy, particularly at medium‐high rain rates in summer. Furthermore, the contributions of multi‐source inputs to the final merging effect was systematically analyzed. It is found that meteorological information, as an auxiliary variable in DML, plays a crucial role in identifying rainy events and adjusting the bias of precipitation estimates, especially over those ungauged regions. This study affirms the call for improving the multi‐source precipitation estimates by combining different machine learning approaches. The new merging precipitation product reported here is recommended for hydrometeorological users of the Tibetan Plateau science community.

High‐resolution hourly precipitation estimates are of vital importance to the hydrological forecast at the basin scale. However, it is still a substantial challenge for data‐deficiency regions to obtain high‐quality precipitation estimates owing to limited ground observations and spatial distributions. In this study, we combined the machine learning technique and an improved side‐scaling algorithm to produce a high‐resolution (1/30°) hourly precipitation data set for a typical lack‐data basin. The proposed approach merges multi‐source information from FY‐4A satellite retrievals, rain gauges, and some precipitation‐related auxiliary variables including temperature, dew point temperature, surface pressure, and soil moisture. Compared to existing mainstream precipitation products, the new merging product DDP‐HAYR demonstrates a distinct advantage in improving detection skills and estimation accuracy of precipitation, particularly in capturing extreme rainfall events, at the hourly scale. Additionally, an explainable Artificial Intelligence (AI) model was employed to quantitatively assess the contributions of input variables to the final precipitation estimates. The integration of both causal and resultant variables (i.e., atmospheric and surface conditions), as demonstrated by the explainable AI model, effectively compensated for the limitations of infrared satellite precipitation products in retrieving extreme precipitation. This novel perspective shows the ability of the machine‐learning model to grasp the mechanisms of precipitation generation and dissipation by training multiple physically constrained variables. In summary, this study offers a typical case study for the hydrological community, which is an example to produce high‐resolution hourly precipitation estimates, which will substantially benefit the operational hydrological modeling, flood forecasting, and water resources monitoring with limited observation data.

The Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) serves as a critical benchmark for calibrating satellite-based precipitation products, with its retrieval quality directly governing the accuracy of global precipitation estimates. While the updated version 07 (DPR-V07) algorithm introduces substantial refinements over its predecessor (DPR-V06), systematic evaluations of its operational advancements in precipitation monitoring remain limited. This study utilizes ground-based rain gauge data from Mainland China (2015–2018) to assess improvements of DPR-V07 over its predecessor’s (DPR-V06) effects. The results indicate that DPR-V07 reduces the high-altitude precipitation underestimation by 5% (vs. V06) in the west (W) and corrects the elevation-linked overestimation via an improved terrain sensitivity. The seasonal analysis demonstrates winter-specific advancements of DPR-V07, with a 3–8% reduction in the miss bias contributing to a lower total bias. However, the algorithm updates yield unintended trade-offs: the High-Sensitivity Scan (HS) mode exhibits significant detection performance degradation, particularly in east (E) and midwest (M) regions, with Critical Success Index (CSI) values decreasing by approximately 0.12 compared to DPR-V06. Furthermore, summer error components show a minimal improvement, suggesting unresolved challenges in warm-season retrieval physics. This study establishes a systematic framework for evaluating precipitation retrieval advancements, providing critical insights for future satellite algorithm development and operational applications in hydrometeorological monitoring.