This study develops an efficient Sliding-Ensemble-based Data Assimilation (SEnDA) scheme and integrates it into the LICOM Forecast System to establish LFS-SEnDA. In SEnDA, time-dependent sliding ensemble samples are used to represent temporally varying background-error covariance structures. Meanwhile, SEnDA has incorporated a parallel-friendly analysis procedure and an accelerated solver to improve computational efficiency. Compared with the conventional Ensemble Optimal Interpolation scheme, SEnDA improves analysis quality, particularly in eddy-rich regions, and achieves about a 30-fold speedup in analysis step. Within the IV-TT Class-4 assessment, LFS-SEnDA ranks among the leading candidate global ocean forecast systems and achieves the highest composite score based on sea surface temperature, sea level anomaly, upper-ocean temperature, and salinity over forecast days 1–6. At a 1-day lead time, LFS-SEnDA achieves global RMSEs of approximately 0.38 °C for sea surface temperature and 0.06 m for sea level anomaly. These results indicate that LFS-SEnDA provides an efficient and scalable framework for improving multi-variate ocean state estimation and global ocean forecasting.