For　flood　simulation　in　humid　catchments,　utilizing　discharge　observations　to　update　the　states　of　hydrological　models　may　enhance　performance.　Asynchronous　Ensemble　Kalman　Filter　(AEnKF),　an　asynchronous　variant　of　the　Ensemble　Kalman　Filter　(EnKF),　holds　substantial　application　potential　in　hydrological　assimilation　due　to　its　ability　to　utilize　more　observations　with　almost　no　additional　computational　time.　This　study　employs　AEnKF　to　update　the　state　variables　of　the　Xin＇anjiang　model,　necessitating　the　use　of　error　models　to　perturb　both　model　states　and　observations　to　generate　ensemble　spread.　The　Bias-corrected　Gaussian　Error　Model　(BGEM)　is　used　to　mitigate　the　systematic　bias　brought　by　perturbating　soil　moisture,　and　the　Maximum　a　Posteriori　Estimation　Method　(MAP)　is　employed　for　the　estimation　of　hyperparameters　of　error　models.　Through　synthetic　and　real-　world　data　testing,　it　has　been　validated　that　the　rectification　of　soil　moisture　perturbations　using　the　BGEM　significantly　reduces　the　systematic　bias　induced　by　Gaussian　perturbations.　Moreover,　the　assimilation　scheme　introduced　in　this　study,　based　on　AEnKF　with　enhanced　error　models,　outperforms　the　EnKF　with　those　models.　It　substantially　reduces　the　accumulation　of　past　errors　in　the　initial　conditions　at　the　start　of　the　forecast,　thereby　aiding　in　elevating　the　accuracy　of　flood　forecasting.