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.

This study provides the first systematic comparison between the Chinese Precipitation Measurement Radar (PMR) and the international Dual-frequency Precipitation Radar (DPR), which mainly focuses on orbital characteristics, precipitation detection accuracy, classification of precipitation types, and vertical precipitation structures. Specifically, how these structures respond to topographic influences was also examined. Our findings indicate that the PMR, owing to its wider swath and higher sampling frequency, achieves substantially greater spatial coverage across mainland China when compared with DPR. Both radars show frequent overlapping scans at high latitudes, with up to eight overlaps within a single year, and a gradual decline in overlap frequency toward lower latitudes. In terms of detection accuracy, DPR generally surpasses PMR. As for identification of precipitation types, however, PMR demonstrates higher sensitivity and broader spatial representation, detecting nearly twice as many events as DPR, particularly excelling in the recognition of stratiform precipitation. In terms of vertical precipitation features, PMR exhibits better accuracy in capturing storm top heights (STH) of convective systems and shows stronger responsiveness to regional and orographic variations. Freezing level heights (FLH) from both instruments are largely consistent, displaying an overall increasing trend with terrain elevation. For bright-band height (BBH), although both radars experience considerable data gaps, PMR delivers more spatially uniform observations comparable to DPR at low and mid-elevation regions, but more physically realistic over high-elevation western areas. In summary, our results suggest that PMR generally offers greater adaptability than DPR in resolving vertical precipitation structures over complex terrain. This strengthens its value as a space-borne observing tool for improving satellite-based precipitation monitoring and deepening the understanding of orographic precipitation processes.