Abstract: In recent years, the accelerated melting of Arctic sea ice has exerted increasing impacts on weather and climate of the Northern Hemisphere. Therefore, accurate prediction of subseasonal sea ice has become an urgent need. In this study, based on the Beijing Climate Center Climate System Model, various coupled data assimilation schemes (atmospheric assimilation, ocean-atmosphere assimilation, ocean-ice-atmosphere assimilation) are used to generate initial conditions for several sets of hindcast experiments (FST-A, FST-AO, and FST-AOI). The impacts of multi-component coupled data assimilation schemes on subseasonal prediction of Arctic sea ice are further investigated based on these hindcast experiments. The results demonstrate that, with the increase in the coupled assimilation components, the prediction skill for sea ice concentration (SIC) and variability is improved. Among all the experiments, FST-AOI shows the highest prediction skill. The impact of ocean-ice-atmosphere assimilation on subseasonal prediction exhibits obvious seasonal differences. Results demonstrate notable reductions in SIC forecast error during most months, particularly within the low-ice season in September and October. However, during the rapid ice-melting process in June and July, the SIC forecast errors in several ice-edge regions (e.g., the Beaufort Sea, the East Siberian Sea, the Laptev Sea, and the Kara Sea) rapidly escalate with increasing forecast time. While multi-component coupled data assimilation effectively improves the subseasonal Arctic sea ice prediction skill, there remains considerable uncertainty regarding its impact during the rapid ice-melt process. Further diagnoses indicate that during the rapid ice-melting process, FST-AOI underestimates SIC near the ice edge in the first week of forecast. Under the influence of the ice-albedo feedback mechanism, this could lead to increased surface shortwave radiation absorption, causing accelerated sea water warming and sea ice melting. As a result, SIC prediction errors rapidly grow in FST-AOI. Future research should focus on enhancing the coordination of coupled data assimilation components to further improve the subseasonal to seasonal prediction skill of Arctic sea ice.