Abstract: Melting layers (ML) during winter precipitation events—characterized by low-altitude, high temporal variability and spatially heterogeneous melting—pose significant challenges to conventional dual-polarization radar hydrometeor classification algorithms (HCAs), resulting in substantially degraded performance in rain-snow transition regions. To address these challenges, we develop an improved HCA scheme using data from 115 dual-polarization radars and vertical profiles from 116 radiosonde stations across China (July 2023—July 2024). The scheme integrates three key components: Optimized spatiotemporal matching with radiosonde observations, real-time ML monitoring through quasi-vertical profile (QVP) analysis, and three-dimensional ML identification using the melting layer detection algorithm (MLDA). These improvements enhance both spatiotemporal precision of ML detection and regional applicability of HCA, ultimately improving the discrimination between various hydrometeor types. The improved scheme effectively resolves issues related to rapid ML variability and spatial heterogeneity in winter, reducing the ML detection update interval from 6—12 h to 6 min and achieving spatial resolution of 1 km in range, 0.1 km in altitude, and 1° in azimuth. Sensitivity experiments using wintertime datasets from seven radars around Nanjing demonstrate that the improved scheme can accurately identify the rain-snow boundary, increasing overall classification accuracy by 11.91% and mixed-phase precipitation accuracy by over 50%. Validation against Present Weather Sensor (PWS) observations confirms that the near-surface classification accuracy exceeds 77% within 100 km. Statistics on winter algorithm activation periods from 115 radars nationwide reveal that the winter-specific algorithm operates for 18%—51% of the year at plain sites in Northeast, North, and Central China, as well as at high-altitude mountain sites, confirming the effectiveness of the improved scheme for wintertime dual-polarization radar hydrometeor classification.