Abstract: In this paper, a comprehensive effect assessment of a convective?stratiform mixed cloud precipitation enhancement operation in Anhui Province on June 17, 2023 was carried out using hourly rainfall data from national and regional automatic rainfall stations in Anhui Province, S-band radar data and sounding data in Anqing area. The results showed that shortly after cloud seeding, a narrow, intense echo area was observed within 1 km above the seeding height. This area expanded 24 minutes after the cloud seeding, formed an intense echo center at the seeding height. Additionally, the echo shape changed from strip to block, accompanied by a concentrated area of severe convection. The proper echo units were identified and tracked by the Centroid Optimization Matching method and the Lagrangian method, and the best comparison unit was selected based on the similarity measurement. Time-series variations of the five radar physical parameters of the seeded unit and the comparison unit were further analyzed. The results showed that following the operation, the radar physical parameter values of the seeded unit increased significantly, reaching their peaks within 42 minutes, and then remained stable. Conversely, the corresponding parameter values in the contrast unit exhibited a decreasing trend. The double-ratio values of the five radar physical parameters were greater than 1 within 1 h after the operation, indicating that the echo intensity of the contrast unit gradually decreased, while the seeded unit developed more vigorously with prolonged lifespan, demonstrating the obvious seeding effect. In addition, we optimized the estimation of natural rainfall in the operational impact area by using a cluster-based historical regression method for the floating area. Firstly, the K-Medoids clustering algorithm combined with PCA dimensionality reduction technology was used to accurately classify the precipitation characteristics in southern Anhui Province. Then the performances of six regression models were evaluated through cross-validation, and the results showed that the ElasticNet regression model had the best performance in predicting the rainfall in the area of influence. Finally, the regression model was applied in the individual cloud seeding case, providing the results of 2.92mm rainfall increase in 3h after operation and 22.3% rainfall enhancement effect, the results of the one-sample t-test showed that the precipitation enhancement effect in the affected area was significant at the 95% confidence level.