Precipitation time series exhibit complex fluctuations and statistical changes. We investigate and forecast precipitation variations in South Korea from 1973 to 2019 using cyclostationary empirical orthogonal function (CSEOF) and regression methods. First, empirical orthogonal function (EOF) and CSEOF analyses are used to examine the periodic changes in the precipitation data. Then, the autoregressive moving average (ARMA) method is applied to the principal component (PC) time series derived from the EOF and CSEOF precipitation analyses. The fifteen leading EOF and CSEOF modes and their corresponding PC time series clearly reflect the spatial distribution and temporal evolution characteristics of the precipitation data. Based on the PC forecasts of the EOF and CSEOF models, the EOF-ARMA composite model and CSEOF-ARMA composite model are used to obtain quantitative precipitation forecasts. The comparison results show that both composite models have good performances and similar accuracies. However, the performance of the CSEOF-ARMA model is better than that of the EOF-ARMA model under various measurements. Therefore, the CSEOF-ARMA composite forecast model can be considered an efficient and feasible technology representing an analytical approach for precipitation forecasting in South Korea.