Abstract: Ensemble forecast has been widely used for daily quantitative precipitation forecast (QPF) for the purposes to analyze forecast uncertainty, to reveal the most possible forecast scenario and to issue warning for extreme weather. Ensemble forecast could be biased; under-dispersion for single model ensemble and/or over-dispersion for multi-model ensemble due to possible problems in the modeling system and physical parameterizations, etc. Therefore, it is necessary to develop a statistical post-process method that can improve the forecasting skills and reliability while retaining the forecast resolution. In the past decade, many methodologies have been developed to generate numerical guidance. For ensemble QPF, the post process methodologies are summarized from three aspects based on their properties, i.e., (1) the nonparametric techniques not based on statistical models, including ensemble QPF bias correction, ensemble or multiple-model QPF integration, and generating probabilistic forecasts from convection-allowing ensembles; (2) the parametric techniques for building statistical models, including the framework of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA); (3) the calibration techniques that account for inter-variable, and spatial and temporal structures, including parametric and experienced copula methods. Finally, we discuss the highlights of precipitation calibration and application, such as the integration of ensembles from different models and different spatial scales, the quality of high resolution precipitation analysis, and a set of reforecasts to provide more reliable training samples. Meanwhile, we need to consider "situation dependent" cases when applying these methodologies, and develop new technologies to handle huge forecast data, capture the extreme events and preserve the dependence structure of the forecast variables.