Fewer perfect prognosis (PP) based statistical downscaling were applied to future projections produced by global circulation models (GCM), when compared with the method of model output statistics (MOS). This study is a trial to use a multiple variable based PP downscaling for summer daily precipitation at many sites in China and to compare with the MOS. For the PP method (denoted as ‘OGB-PP’), predictors for each site are screened from surface-level variables in ERA-Interim reanalysis by an optimal grid-box method, then the biases in predictors are corrected and fitted to generalized linear models to downscale daily precipitation. The historical and the future simulations under the medium emission scenario (often represented as ‘RCP4.5’), produced by three GCMs (CanESM2, HadGEM2-ES and GFDL-ESM2G) in the coupled model intercomparison project phase five (CMIP5) were used as the downscaling bases. The bias correction based MOS downscaling (denoted as ‘BC-MOS’) were used to compare with the OGB-PP. The OGB-PP generally produced the climatological mean of summer precipitation across China, based on both ERAI and CMIP5 historical simulations. The downscaled spatial patterns of long-term changes are diverse, depending on the different GCMs, different predictor-bias corrections, and the choices on selecting PP and MOS. The annual variations downscaled by OGB-PP have small differences among the choices of different predictor-bias corrections, but have large difference to that downscaled by BC-MOS. The future changes downscaled from each GCM are sensitive to the bias corrections on predictors. The overall change patterns in some OGB-PP results on future projections produced similar trends as those projected by other multiple-model downscaling in CMIP5, while the result of the BC-MOS on the same GCMs did not, implying that PP methods may be promising. OGB-PP produced more significant increasing/decreasing trends and larger spatial variability of trends than the BC-MOS methods did. The reason maybe that in OGB-PP the independent precipitation modeling mechanism and the freely selected grid-box predictors can give rise to more diverse outputs over different sites than that from BC-MOS, which can contribute additional local variability.