To improve the radar quantitative precipitation estimation, an optimization rainfall algorithm of Sband dualpolarization radar, named HCALIQ, based on hydrometeor identification is developed by referring to the Colorado State University (CSU)ICE algorithm in this study. The radar estimator R (ZH), R (ZH, ZDR), R (KDP) calculated from the raindrop size distribution data collected in South China are used in this algorithm. Both the data collected from the Sband dualpolarization radar in Zhuhai, Guangdong Province and a network of rain gauges are used to evaluate the performance of the new algorithm. Comparison is also performed between the HCALIQ and CSUICE optimization algorithms and the traditional R (ZH) method. The results show that the HCALIQ optimization algorithm is well correlated with gauges and presents high stability. In addition, the distribution of hourly accumulation bias has light relation with the distance from the radar. The estimation results of the precipitation events show that two kinds of optimization algorithms are obviously superior to the traditional R (ZH) method for convective precipitation; the R (ZH) method is better than the two optimization algorithms for mixed cloud precipitation; the three errors statistics of the HCALIQ optimization algorithm are superior to the CSUICE algorithm. According to the bias statistics of the classification of rainfall intensity, the new HCALIQ optimization algorithm bias decreases by 23% for light rain 71% for heavy rain and 68% for torrential rain respectively in comparison to the traditional R (ZH) method.