Beyond conventional weather forecasts and seasonal climate predictions,a continuum of prediction across all timescales,known as weather-climate seamless prediction,has garnered considerable attention and progressed over the last decades.However,among predictions on various timescales,subseasonal predictions,bridging weather forecasts and short-term climate predictions still face challenges.This timescale typically extends beyond two weeks but falls short of a season,where the influence of initial conditions has faded away while the forcing from boundary conditions remains insignificant.Consequently,understanding the sources of subseasonal predictability and achieving effective subseasonal predictions encounter significant challenges.As reported in numerous previous studies,tropical intraseasonal oscillations (ISO),comprising the Madden-Julian Oscillation (MJO) and Boreal Summer Intraseasonal Oscillation (BSISO),can provide dominant sources of global subseasonal-to-seasonal (S2S) predictability.Based on the subseasonal climate forecast system of Nanjing University of Information Science and Technology (NUIST CFS1.1),the atmospheric initialization of individual members and ensemble strategy are slightly modified toward an upgraded version named NUIST CFS1.1 Pro,which consists of nine members and saves computational costs.Furthermore,using the real-time multivariate MJO index and two BSISO indices,BSISO1 and BSISO2,the prediction skills of tropical ISOs during different seasons are evaluated.The results show that skillful prediction (ACC>0.5) for MJO,BSISO1,and BSISO2 can extend to 26,17,and 12 lead days,respectively,and for strong events (amplitude> 1),it can be extended to 30,21,and 13 lead days,respectively.In predicting these tropical ISOs,the NUIST CFS1.1 Pro outperforms two newly-developed subseasonal forecast systems in China (i.e.,BCC_CSM2 and FGOALS-f2).Moreover,it achieves competitive performances compared to eight major operational prediction systems participating in the international S2S project,with a relatively leading level in predicting winter MJO and summer BSISO1,as well as a medium level in predicting BSISO2.As for the winter MJO and BSISO1 predictions,the target phases 2,3,6,and 7 display higher skills than the other four phases.Further analysis indicates that the NUIST CFS1.1 Pro can accurately capture the eastward propagation of the winter MJO at lead times of 21—25 days.Additionally,it partly predicts the MJO-related 2 m temperature anomalies in China,particularly the cold anomalies during phases 2 and 3.In summer,the NUIST CFS1.1 Pro well predicts the northward and northwestward propagation of BSISO1 at lead times of 16—20 days,especially anomalous convection and low-level circulation over the northwestern Pacific.This leads to successful predictions of the spatial pattern of precipitation anomalies in East China associated with BSISO1.However,the predictions of NUIST CFS1.1 Pro on these time scales severely underestimate tropical ISO signals and their impacts on air temperature and precipitation over China,warranting further efforts for improvement.In particular,there is ample room for the NUIST CFS1.1 to improve in the prediction of MJO teleconnections over China during the winter.For instance,the warm anomalies over most of China in phases 6 and 7 of the MJO cannot be successfully predicted,whereas the prediction of tropical convection and circulation displays good skill.