In this study, we attempted to combine the sensitive area for target observation associated with El Niño- Southern Oscillation predictions with multimodel ensemble forecast methods and proposed a new method that provides robust forecast skill with smaller computational cost. Specifically, this new method allows for superensemble prediction (SUP) with unequal weighting and higher skill to be used in perturbation sensitive areas and for the bias-removed ensemble mean with equal weighting to be used in other regions. By using the monthly mean data of pre-industrial control simulations of 15 models in Phase 5 of the Coupled Model Intercomparison Project experiments, we compared this new method with the existing multimodel ensemble forecast methods preliminarily under the ideal forecast experiments of tropical Pacific sea surface temperatures (SSTs) in various forecast periods. The results show that in the forecast period of 1-20 years, the proposed method has high forecast skill similar to that when using the SUP with SSTs in the entire tropical Pacific. The new method greatly reduces the computational cost, and the computation time is only one-fourth of that in the SUP. Therefore, the new multimodel ensemble forecast methods are efficient and have more accurate forecast skill, which further demonstrates that the tropical Pacific SST forecasts are extremely sensitive to model errors in the sensitive region. Thus, the new method has a good performance in prediction skill.