In this paper, we used a nonhomogeneous Gaussian regression model (NGR) as the post-processing techniques to calibrate probabilistic forecasts that take the form of probability density functions for temperature. We also performed the alternative implementation techniques of NGR, which are station-specific ensemble model output statistics (EMOS) model. These techniques were applied to forecast temperature over Pyeongchang area using 24-member Ensemble Prediction System for Global (EPSG). The results showed that the station-specific EMOS model performed better than the raw ensemble and EMOS model.