This study uses a Bayesian approach to merge ensemble seasonal climate forecasts generated by multiple climate models for better probabilistic and deterministic forecasting. Within the Bayesian framework, the climatological distribution of the variable of interest serves as the prior, and the likelihood function is developed with a weighted linear regression between the climate model hindcasts and the corresponding observations. The resulting posterior distribution is the merged forecast, which represents our best estimate of the variable, including its mean and variance, given the current model forecast and knowledge about the model’s performance. The handling of multimodel climate forecasts and nonnormal distributed variables, such as precipitation, are two important extensions toward the application of the Bayesian merging approach for seasonal hydrological predictions. Two examples are presented as follows: seasonal forecast of sea surface temperature over equatorial Pacific and precipitation forecast over the Ohio River basin. Cross validation of these forecasts shows smaller root mean square error and smaller ranked probability score for the merged forecast as compared with raw forecasts from climate models and the climatological forecast, indicating an improvement in both deterministic and probabilistic forecast skills. Therefore there is great potential to apply this method to seasonal hydrological forecasting.