Abstract: Four objective quantitative precipitation forecast (QPF) calibration methods, including frequency matching method (FMM), optimal threat score (OTS), optimal percentile (OP) and probability matching (PM), are comprehensively verified based on annual and seasonal ECMWF QPFs at different forecast lead times. An ideal model is proposed to study the performance of FMM and OTS under different scenarios of spatial displacement and dry/wet biases. A heavy rain case is used to demonstrate basic characteristics of the four different calibration methods. Results show that FMM and OTS can only adjust the magnitude of deterministic QPF, while OP and PM can change the pattern of QPF to some extent by using ensemble forecast information. Aiming at optimizing the frequency bias, FMM can well eliminate the dry/wet bias of QPFs, but it can only improve the threat score (TS) of original QPFs when the displacement error is small and the dry/wet bias is large. OTS has limited skills in improving the TS of moderate rain with weak wet bias. By contrast, OP can improve the TS of all precipitation thresholds, benefiting from using ensemble forecast information, especially for longer forecast lead times. However, OP shows large wet biases during spring and summer seasons, while PM suffers from large dry biases for torrential rain events due to the lack of historical observation information. The evaluation of economic value model shows that OP has relatively higher reference value for torrential rain in risk decision making.