Enhancing Forecast Skill of Winter Temperature of East Asia Using Teleconnection Patterns Simulated by GloSea5 Seasonal Forecast Model

GloSea5, a seasonal forecast system of the UK Met Office, shows reasonable skill among state-of-the-art operational systems. However, average surface temperature (T2m) in winter (December–February) GloSea5 is particularly low East Asia. To improve over Asia, we focused on high score global teleconnection patterns simulated by GloSea5. Among well-predicted patterns, selected those highly correlated with Asian T2m: Atlantic (EA), Polar/Eurasia (PE), Atlantic/Western Russia (EAWR), and West Pacific (WP) patterns. A multiple linear regression model was constructed using indices as predictors. These results are promising. The statistical skill-score evaluation anomaly correlation coefficient (ACC), root mean squared error (RMSE), mean-squared (MSSS) showed an improvement predicted T2m where values ACC MSSS increased 0.25 0.37, respectively, RMSE decreased 0.63 compared to dynamic results. suggest that well-designed combined dynamical approach for prediction can be beneficial some regions predictability exhibits value.