In this study a simplified initialization scheme, which is off-line, is proposed and applied to an oceanic general circulation model (OGCM) for El Ni¿o--Southern Oscillation (ENSO) prediction. The initialization scheme is based on the National Centers for Environmental Prediction ocean reanalysis and a two-dimensional variational (2D-Var) assimilation algorithm. It focuses on two basic issues in data assimilation: observed data and computational cost. Compared with a traditional assimilation system, this simplified scheme avoids model forward integration and the complications of acquiring and processing raw in situ temperature observations. The off-line scheme only requires around 1/20 of the computational expense of a traditional algorithm. Two hybrid coupled models, an OGCM coupled to a statistical atmosphere, and the same ocean model coupled to a dynamical atmosphere, were used to examine the initialization scheme. A large ensemble of prediction experiments during the period from 1981 to 1998 shows that relative to just a wind forced initialization the off-line scheme leads to a significant improvement in predictive skills of Ni¿o3 sea surface temperature anomaly (SSTA) for all lead times. The prediction skills obtained by the scheme is as high as that attained by a more traditional on-line assimilation scheme.