In present operating systems, indirect assimilation is frequently used to assimilate the radar reflectivity factor to avoid the problems caused by the linearization of the observation operator. Based on a real-time background-dependent radar reflectivity factor indirect assimilation scheme, cycling assimilation and forecasting experiments of four heavy rainfall processes (two convective and two frontal) were carried out. The results show that compared with the traditional temperature-determination scheme, the background-dependent scheme has smaller temperature forecast errors and higher precipitation forecast scores for the severe convective rainfall cases, but the difference in frontal process is not obvious. Further analysis shows that for severe convective rainfall, the background-dependent scheme introduced real-time background information when assimilating the reflectivity factor, allowing the hydrometeor structure of the analysis field to characterize the actual convective characteristics better and be more coordinated with other model variables, thereby improving the thermal, dynamic, and humidity conditions of the model forecast, thus improving precipitation forecasting. For heavy frontal rainfall, the hydrometeor structural difference in the analysis field of the two schemes is not obvious; thus, the difference in precipitation forecast is small.