Seasonal rainfall forecasts are one of the most important part of water resources management in minimizing climate-related risk. Recently, abnormal change in precipitation raised the attention of not only scientists it gets big interest in general public too. Seasonal climate forecasts are typically based on simulations from general circulation models (GCMs) that approximate the complex physical, chemical, and biological processes. But it has been known that General Circulation Models have considerable uncertainties. Recent studies suggested that Multi-Model Ensemble(MME) could reduce this uncertainties and give an improvement on the results. There have been used several MME estimation techniques that are simply averaging models and regression based techniques. This study aims to improve MME using Bayesian Model Averaging(BMA) technique which gives weights to the models based on each model performance to present observation. The result showed that BMA technique output is statistically more fitted to the observation than the other techniques and it is very important to further analysis such as downscaling and other simulation method that uses future precipitation as a main input data.

• Uranchimeg, Sumiya(Student Member. Master’s Degree, Department of Civil Engineering, Chonbuk National University) | 오랑치맥 솜야
• Kwon, Hyun-Han(Member, Associate Professor, Department of Civil Engineering, Chonbuk National University) | 권현한 Corresponding Author
• Yoon, Sun-Kwon(Research Fellow, Climate Change Research Team, Climate Research Department, APEC Climate Center) | 윤선권