Abstract: Efficiency and stability of cascade reservoirs in hydropower generation can be improved by using the data of quantitative precipitation forecasts (QPFs), but the improvements are affected by high uncertainties in QPFs. To enhance the usefulness of inflow forecasts resorting to QPFs, precipitation forecasts were divided into several levels and the probability distributions of the actual precipitation at different levels analyzed in this study. Then, a parameter-simulation-optimization (PSO) model and hedging rule curves (HRCs) were used to construct a PSO-HRCs model for hydropower operation, which fuses the reservoir storage and inflow as a single variable and thus makes the solution computationally tractable. Based on the probability distributions, the PSO-HRCs model can generate precipitation, inflow and operation policy for different probabilities, and hence the corresponding risks and losses in water releasing and storing schemes can be evaluated. This paper presents a case study of the Hun River cascade reservoir system that used the QPFs of ten days lead time, issued by the Global Forecast System (GFS), the U.S. National Centers for Environmental Prediction, to analyze and compare the efficiency and stability of hydropower generation with different probabilities.