Abstract: Background Error Covariance (BEC) is a crucial component of variational data assimilation frameworks. Constructing a BEC that more accurately represents reality is essential for enhancing the assimilation and forecasting capabilities of numerical prediction systems. Based on the CMA-MESO kilometer-scale regional numerical prediction system, diurnal variations of BEC parameters are analyzed, and the variational parameters are applied in actual assimilation and forecasting experiments to assess their impacts. The ensemble method is used to calculate background error samples, and BEC parameters are statistically analyzed at eight times of a day with a 3 h interval (00: 00—21: 00 UTC) to investigate their diurnal variations. The results show that the background Root Mean Square Error (RMSE) and spatial correlation scale of the background error for various variables exhibit clear diurnal variation features in the lower and middle troposphere. The RMSEs of wind and humidity fields are generally larger at night than during the day, with the maximum values occurring at 12: 00 UTC. For temperature, the RMSE is larger at 06: 00 and 09: 00 UTC with more pronounced variations below 850 hPa. Regarding horizontal correlation, larger correlation scales are observed during 18: 00—03: 00 UTC, while smaller correlation scales are found during 06: 00—15: 00 UTC when vertical convective mixing is stronger. As for vertical correlation coefficient of the background error, the diurnal variation is most prominent at 06: 00 UTC, with smaller differences at other times. The idealized experiment results demonstrate that the newly estimated diurnal variation parameters can adjust the influence weights and propagation distances of observational information at different times, ensuring that the assimilation analysis matches the diurnal variation characteristics of BEC. Month-long assimilation and forecasting cycle experiments show that using the diurnal variation BEC parameters reduces assimilation analysis errors in wind and temperature fields, improves precipitation forecasts-particularly for heavy rain and thunderstorms and also reduces 2 m surface temperature forecast errors.