In this study, a statistical prediction model has been developed to forecast monthly Sea Surface Temperature (SST) in the Indian Ocean. It is a linear regression model based on a lagged relationship between the Indian Ocean SST and the NINO3 SST. A new approach to the statistical modeling has been tried out, in which the model predictors are obtained from not only observed NINO3 SST but also predicted results produced by a dynamical El Ni¿o model. The forecast skill of the present model is better than that of persistence prediction. In particular, the present model has a significantly improved predictive skill during the spring and summer seasons when the boreal summer Indian monsoon is affected by the Indian Ocean SST.