Background error covariance plays an essential role in data assimilation systems, particularly in variational assimilation systems. The National Meteorological Center (NMC) method has widely been used to generate forecast error samples for estimating background error covariance. Currently, most variational-based rapid update and cycling (RUC) data assimilation and forecasting systems use a fixed background error covariance at each analysis moment to reduce computational costs. However, with the increasing frequency of assimilation in the RUC data assimilation and forecasting systems, a fixed background error covariance may not be suitable for all analysis moments. To adopt diurnal background error covariance in the RUC data assimilation and forecasting system more reasonably, the diurnal background error covariance characteristics in summer and winter are analyzed by the NMC method based on the RMAPS-ST system, and assimilation and forecast experiments are conducted. The results show that the background error covariances in summer and winter exhibit obvious diurnal characteristics. The standard deviation of forecast error samples and the eigenvalues of each control variable (U, V, T, and RH_s) are higher at night than during the day, indicating that the forecast errors of the model system are more significant at night than during the day. Meanwhile, the standard deviation of forecast error samples and the eigenvalues of each control variable are higher in summer than in winter, suggesting that the model forecast errors of the system are greater in summer than in winter. The horizontal length scale is generally larger in summer than in winter, which may be because the spatial integrity of the RMAPS-ST system forecast error is more consistent in summer and the horizontal correlation is higher, leading to a larger length scale. The 3-day cycling experiments initially indicate that the use of diurnal background error covariances can improve the assimilation and forecast of the U, V, T, and Q fields of RMAPS-ST system, thereby enhancing the performance of precipitation forecasts.