Dynamical downscaling is a widely-used approach to generate regional projections of future climate extremes at a finer scale. Previous studies indicated that the global climate model (GCM) bias correction method prior to dynamical downscaling can improve the simulation of the climate extreme to a certain extent. Recently, a new bias correction method termed MVT was developed. Note that this method did not correct the GCM biases of the climate extreme event explicitly. In this study, we evaluate the MVT method in terms of various climate extreme events through three dynamical downscaling simulations over Asia-western North Pacific with 25 km grid spacing throughout 1980–2014, and further investigate to what extent and how this bias correction method can improve the simulation of downscaled climate extreme events. The dynamical downscaling simulations driven by the original GCM dataset derived from the MPI-ESM1-2-HR (hereafter WRF_GCM), the bias-corrected GCM (hereafter WRF_GCMbc) are validated against that driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 dataset, respectively. The results suggest that compared with the WRF_GCM, the WRF_GCMbc shows more than 26% decrease in root mean square errors of the precipitation and temperature extreme indices, and even 61% out of seasonal extreme indices show more than 50% reduction. Such improvements in the WRF_GCMbc are primarily caused by the correct simulation of the large-scale circulation due to the GCM bias correction. The large-scale circulation in turn improves the simulation of the precipitation and cloud by the water vapor transport and further improves the simulation of the 2m temperature by the radiation process and the surface energy balance, which contribute to the better simulation of the precipitation and temperature extreme indices.