Abstract: Understanding the classification of rain types is crucial for analyzing the microphysical characteristics of regional precipitation, constructing multisource precipitation fusion error models, and enhancing radar-based quantitative precipitation estimation. This study proposes a radar-based rain-type classification method tailored for the Nanjing area using data from C-band dual-polarization radar and raindrop disdrometer observations collected by the Nanjing University of Information Science and Technology from 2015 to 2016. The results were compared and verified with other classification methods. First, 36 typical stratiform and convective precipitation processes were filtered out based on the time-series data of rainfall rates from raindrop disdrometers, ground-based radar reflectivity factor plane position indication, and time–height indication. The DSD (raindrop size distribution) parameters of typical stratiform (convective) precipitation at the three disdrometer stations were statistically analyzed, and a precipitation type classification line suitable for the Nanjing area was fitted. Apply the classification line, fitted based on raindrop disdrometer data, to the DSD parameters retrieved from ground-based radar using a variational method for ground-based radar precipitation type classification. By analyzing the time–height distribution of the separation index for precipitation types in typical stratiform (convective) processes and comparing with the precipitation classification products from the DPR (dual-frequency precipitation radar) on satellites, the classification effectiveness was validated. Finally, the classification results were applied to quantitative radar-classification precipitation estimation to further illustrate the application of precipitation classification. The results show that the fitting classification lines of the three raindrop disdrometer stations in Nanjing were consistent. Stratiform and convective precipitation processes could be effectively separated by this line. A comparative analysis with the DPR precipitation classification products revealed that the proposed classification method for Nanjing showed higher accuracy in distinguishing between stratiform and convective precipitation compared to other typical precipitation classification methods, with recognition rates for stratiform and convective precipitation of 84.56% and 72.64%, respectively. Furthermore, radar quantitative precipitation estimation based on this rain-type classification demonstrated better performance than traditional unclassified rain measurement formulas. The rainfall estimation formula R(K_DP) based on the specific differential phase demonstrated the best overall performance among the four classification rainfall estimation formulas. The formula R(Z_H) based on the horizontal reflectivity factor showed the best performance in stratiform cloud precipitation retrieval, while the formula R(K_DP) based on the specific differential phaseperformed best in convective cloud precipitation retrieval. The rainfall estimation formula R(Z_H, Z_DR) based on both the horizontal reflectivity factor and the differential reflectivity showed the most significant improvement in precipitation accuracy compared to the original overall rainfall estimation formula.