An empirical orthogonal function analysis has been applied to solving the forecast problem of the monthly mean sea surface temperature for the East China Sea and the adjacent waters. The data matrix of the original sea surface temperature fields can be separated into two components, /’. e. the spatial and the temporal components. According to the properties of its spatial component that almost does not change with time and through the extrapolation of its temporal component, the prediction for large area sea surface temperature will be achieved. The time coefficients for temporal component are predicted by means of traverse and vertical time series method.On the basis of forecasting for these two years, it has been proved that the method objectively reflected the internal relations and interactions of sea surface temperature among the stations of water area. The results of the suggested method are better than the predicted method for a collection of each individual stations. The mean absolute error of p An empirical orthogonal function analysis has been applied to solving the forecast problem of the monthly mean sea surface temperature for the East China Sea and the adjacent waters. The data matrix of the original sea surface temperature fields can be separated into two components, / ’. e. the spatial and the temporal components. According to the properties of its spatial component that does not change with time and through the extrapolation of its temporal component, the prediction for large area sea surface temperature will be achieved. The time coefficients for temporal component are predicted by means of traverse and vertical time series method. On the basis of forecasting for these two years, it has been proved that the objective objectively reflected the internal relations and interactions of sea surface temperature among the stations of water area. of the suggested method are better than the predicted method for a collection of each individual stations. The mean a bsolute error of p