Young‐Mi Min

This study comprehensively assesses the seasonal prediction skill of precipitation over the Middle East (ME), with a particular focus on the Arabian Peninsula (AP), using the APEC Climate Center (APCC) multi‐model ensemble (MME) system. Analysis of one‐month lead hindcasts (1993–2016) shows that the MME consistently outperforms individual models and shows higher skill during boreal spring and autumn. These seasons exhibit strong teleconnections with tropical SST‐based climate drivers, such as El Niño‐Southern Oscillation (ENSO), El Niño Modoki, and the Indian Ocean Dipole (IOD), while forecast skill remains lower in boreal summer and winter. Teleconnection analyses reveal that the MME's skill is closely linked to its ability to capture SST‐driven precipitation variability. Forecast skill significantly declines when regression‐based precipitation signals from climate drivers are removed, highlighting the importance of these teleconnections. Although the MME exhibits overall superior performance, challenges remain in simulating winter precipitation over the AP, where the influence of climate drivers is relatively weak and models struggle to accurately reproduce teleconnection patterns. These findings underscore the value of improving the model representation of teleconnections to enhance seasonal forecast reliability. The results provide actionable insights for enhancing dynamical prediction systems and inform future model development tailored to this climate‐sensitive region.

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.

Seasonal forecasts are commonly issued in the form of anomalies, which are departures from the average over a specified multiyear reference period (climatology). The model climatology is estimated as the average of the retrospective forecasts over the hindcast period. However, different operational centers that provide seasonal ensemble predictions use different hindcast periods based on their model climatology. Additionally, the hindcast periods of recently developed and upgraded newer models have shifted in the recent years. In this paper, we discuss the recent challenges faced by APCC multi‐model ensemble (MME) operations, especially changes in the hindcast period for individual models. Based on the results of various experiments for MME prediction, we propose changing the hindcast period, which is the most appropriate solution for APCC operation. This makes the newly developed models join the MME and increases the total number of participating models, which facilitates the skill improvement of the MME prediction.

Seasonal forecasts are commonly issued in the form of anomalies as departures from average in a specified multiyear reference period (climatology). The model climatology is estimated as average of retrospective forecasts over the hindcast period. However, different operational centers providing seasonal ensemble predictions use different hindcast periods based on their own model climatology. In addition, the hindcast period of recently developed/upgraded newer models tends to shift to the recent years. In this paper, we discuss recent challenges faced by the APCC multi-model ensemble (MME) operations, especially changes in the hindcast period for individual models. Based on the results of various sensitivity experiments for the MME prediction, we proposed to change the hindcast period that is the most appropriate solution for the APCC operations. It makes the newly developed models join the MME and increase the total number of participating models, which facilitates the skill improvement of the MME prediction.

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.