Since 2007, the Asia‐Pacific Economic Cooperation (APEC) Climate Center (APCC) has monthly issued multimodel ensemble (MME) seasonal predictions for 3 months, with 1 month lead time, and disseminated it to APEC member economies. This paper gives a comprehensive documentation of the current status of the APCC operational multimodel performance, with a large set of retrospective (1983–2003) and real‐time (2008–2013) predictions of temperature and precipitation. In order to investigate the enhancement in seasonal predictability that can be achieved by empirically weighted MME (using multiple regression) and calibrated MME (by correcting single‐model prediction using a stepwise pattern projection method) schemes, operationally implemented at the APCC, we compare them with a simple averaged MME (with equal weightings), for predicting seasonal mean temperature and precipitation 1 month ahead. The results indicate that the simple averaged MME consistently outperforms the multiple regression‐based MMEs, when considering all aspects of the predictions from operational prediction systems (i.e., in different variables, regions, and seasons) whereas the calibrated MME shows the capability to reduce errors and improve forecast skills in a large proportion of cases. The possible causes of the failure and success of the different MME methods implemented in the APCC operations are discussed.

Since its establishment in 2005, the APEC Climate Center (APCC) has pioneered advancements in seasonal climate prediction through its Multi‐Model Ensemble (MME) system, integrating the world's most diverse range of dynamical climate models. Over the past two decades, APCC has incorporated long‐range forecasts from more than 60 model versions, contributed by 21 institutions across 11 countries. This study presents the first diachronic assessment of the APCC MME system evolution, focusing on operational model transitions and associated substantial 34% improvement in global forecast skill. Despite these significant advances, challenges persist, particularly in predicting precipitation over land areas in the northern extratropics and in overcoming the spring predictability barriers. Our findings underscore the crucial role of model innovation, increased diversity, and international collaboration in advancing seasonal prediction. As the first study of its kind, this work provides key insights into the evolution of climate modeling, providing a foundation for future forecasting improvements.