With the development of the weather forecast modernization, forecasters are facing challenges brought by meteorological data explosion, the increasing demand of the service front end as well as the wide use of objective forecasting technology. Traditional quantitative precipitation forecast (QPF) technology, which is mainly based on manually plotting precipitation areas, can no longer assist forecasters to demonstrate added value at higher levels. To support the forecasters’ central role in the QPF procedure, a subjective and objective QPF blender was designed and developed. This platform helps forecasters to take control of the whole process of numerical forecast from the following five aspects: selection from mass forecast data, integration of multisource QPF, adjustment and correction of QPF, grid processing and service product production. The intelligence of the platform is secured by the development of a number of key supporting techniques, including multimodel QPF dataset construction technology, multimodel QPF integration technology, QPF field adjustment and correction techniques and gridded QPF postprocessing technology. Based on MICAPS4, the main functions of this QPF platform has been realized. The “QPF Master Blender 1.0” version was released and put into operation in May 2017, which has obtained good feedback and effectiveness. By the end of this paper, the future development of the platform is prospected, including the development of numerical model verification tools to support forecasters to make the best judgments, and research on the fusion technologies of multiscale model information.