In this paper, a regional climate model (Seoul National University Regional Climate Model (SNURCM)) was configured by coupling an advanced land surface model with the Penn State University/National Center for Atmospheric Research mesoscale model. A spectral nudging technique was also implemented to reduce systematic biases resulting from the incomplete treatment of the downscaling problem in the classical lateral boundary condition. To evaluate the capability of the SNURCM, the East Asian summer monsoon (EASM) cases in 1991 and 1994 were simulated. The SNURCM simulated the large anomalous features in the summers of 1991 and 1994, in terms of precipitation and atmospheric circulations. The model also reproduced regional details in the horizontal distribution of precipitation with a certain degree of systematic biases. The model tended to simulate different atmospheric vertical structures over land versus ocean. Surface air temperature was also simulated reasonably in terms of spatial pattern, but a cold bias appeared over most of East Asia. The atmospheric circulations associated with the monsoon precipitation were well captured in the simulations, and the temporal evolutions of monsoon circulation and precipitation were also reasonably simulated. We also analyzed the hydrology and energy budget to investigate the reasons for the systematic model bias and extreme climate events occurring in the summer of 1991 and 1994. An experiment without the spectral nudging for the 1991 EASM case was also performed to assess the impacts of spectral nudging in the EASM simulation. The spectral nudging technique played a significant role in reducing the deviation of the model solutions from large-scale driving forces. It contributes to improve model performance not only in simulating the mean features but also in capturing individual weather precipitation events. Without the spectral nudging, the weakened subtropical high did not simulate the low-level confluent monsoon flows properly near the monsoon precipitation areas, and consequently resulted in significant precipitation bias from observation.