CSEOF analysis is conducted on the daily mean, maximum, and minimum temperatures measured at 60 Korea Meteorological Administration stations in the period of 1979-2014. Each PC time series is detrended and fitted to an autoregressive (AR) model. The resulting AR models are used to generate 100 sets of synthetic PC time series for the period of 1979-2064, and the linear trends are added back to the resulting PC time series. Then, 100 sets of synthetic daily temperatures are produced by using the synthetic PC time series together with the The cyclostationary EOF (CSEOF) loading vectors. The statistics of the synthetic daily temperatures are similar to those of the original data during the observational period (1979-2064). Based on the synthetic datasets, future statistics including distribution of extreme temperatures and the length of four seasons have been analyzed. Average daily temperature in spring is expected to decrease by a small amount, whereas average temperatures in summer, fall and winter are expected to increase. Standard deviation of daily temperatures is expected to increase in all four seasons. The Generalized Extreme Value and Generalized Pareto distributions of extreme temperatures indicate that both warm and cold extremes are likely to increase in summer, while only warm extremes are predicted to increase significantly in winter. Thus, heat waves will increase and cold waves will decrease in number in future. Spring and fall will be shorter, whereas summer and winter will be longer. A statistical prediction carried out in the present study may serve as a baseline solution for numerical predictions using complex models.