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This study assesses the forecast skill of eight North American Multimodel Ensemble (NMME) models in predicting Nino-3/-3.4 indices and improves their skill using Bayesian updating (BU). The forecast skill that is obtained using the ensemblemean ofNMME (NMME-EM) shows a strong dependence on lead (initial) month and target month and is quite promising in terms of correlation, root-mean-square error (RMSE), standard deviation ratio (SDRatio), and probabilistic Brier skill score, especially at short leadmonths. However, the skill decreases in target months from late spring to summer owing to the spring predictability barrier. When BU is applied to eight NMME models (BU-Model), the forecasts tend to outperform NMME-EM in predicting Nino-3/-3.4 in terms of correlation, RMSE, and SDRatio. For Nino-3.4, the BU-Model outperforms NMME-EM forecasts for almost all leads (1-12; particularly for short leads) and target months (from January to December). However, for Nino-3, the BU-Model does not outperform NMME-EM forecasts for leads 7-11 and target months from June to October in terms of correlation and RMSE. Last, the authors test further potential improvements by preselecting "good'' models (BU-Model-0.3) and by using principal component analysis to remove the multicollinearity among models, but these additional methodologies do not outperform the BU-Model, which produces the best forecasts of Nino-3/-3.4 for the 2015/16 El Nino event.