Based on the JAMSTEC SINTEX-F model,this paper developed NUIST climate forecast system 1.0 version (NUIST CFS1.0) for seasonal-to-multiyear forecasts of global climate anomalies.The 9-member ensemble hindcast experiments during 1982-2018 were conducted using the NUIST super-computer.The assessment of the hindcasts shows that NUIST CFS1.0 displays useful skills in predicting sea surface temperature anomalies in the tropical Pacific and Indian Ocean.In particular,ENSO (Niño3.4 index) is skillfully predicted up to 1.5 to 2 years in advance.And the Indian Ocean Dipole (IOD) can be predicted 1-2 seasons in advance.It shows good prediction skills for major tropical climate signals.However,prediction of the East Asia climate is rather poor,which is a long-standing problem in almost all current and past dynamical prediction systems.Considering the large impact of ENSO on East Asia climate,this paper adopted a simple method to improve prediction of NUIST CFS1.0 by correcting the systematic biases of model in predicting the impact of ENSO on climate in East Asia in summer.The results suggest that this simple correction method can improve both the hindcast and real time forecasts of surface air temperature and precipitation anomaly percentage in China.The spatial pattern correlations of the two variables in China are increased to some degree.Further improvement of the forecast system and correction methods are under development.In addition,a preliminary assessment of the climate prediction in winter and spring is performed.Real time forecasts of the temperature anomalies and precipitation anomaly percentages in China during winter 2019/2020 and spring 2020 are provided.