Initial perturbations play a crucial role in determining the performance of an ensemble prediction system (EPS). This study comprehensively compares three types of initial perturbations—generated by ensemble data assimilation (EDA), singular vectors (SV), and their hybrid EDA–SV—using the China Meteorological Administration (CMA) global forecast model. In addition to conventional ensemble verification metrics, diagnostic tools such as kinetic energy (KE) spectrum analysis and spatial filtering are employed. Compared with the SV-based perturbations currently used operationally in the CMA global EPS (CMA-GEPS), EDA-based perturbations exhibit more smaller-scale structures and higher global perturbation KE, particularly in the Tropics. Ensemble forecasting experiments reveal that the EDA method provides superior ensemble spread and perturbation KE in the Tropics during the early forecast period. However, the SV method performs better in the extratropics throughout the forecast period and in the Tropics during the mid-to-late forecast period. The EDA–SV approach improves the overall performance of CMA-GEPS, yielding better spread–error relationships and enhanced forecast skill compared with SV and EDA methods. Results from spatially filtered ensembles further show that EDA–SV combines the subsynoptic-scale and mesoscale advantages of EDA-based perturbations in the Tropics, with the large-scale and synoptic-scale strengths of SV-based perturbations across the globe. This synergy leads to superior performance across spatial scales and lead times in both tropical and extratropical regions. Consequently, the EDA–SV method is planned for implementation in the next upgrade of the CMA-GEPS.

Madden–Julian oscillation (MJO) is one of the dominant sources of extended-range atmospheric predictability. However, many atmospheric numerical models fail to predict MJO owing to limitations in representing air–sea interaction processes. This study investigated the role of sea surface temperature (SST) forcing in improving MJO predictability using China Meteorological Administration uncoupled Global Ensemble Prediction System (CMA-GEPS), extending forecasts from 16 to 35 days for the first time by applying different SST forcing schemes. Three SST forcing schemes are examined: (1) CTL SST, which uses fixed SST forcing, (2) E-Folding SST, which adjusts initial analyses toward observed climatology, relying primarily on the historical observations, and (3) Two-Tiered SST, which combines the bias-corrected SST analysis and predictions from the CMA coupled prediction system. The results confirmed that each SST forcing scheme is reasonably designed, with the Two-Tiered scheme providing better SST forcing information and more realistic equatorial wave features. For MJO prediction, the Two-Tiered scheme exhibits superior skill, particularly with strong MJO initialization, extending predictability to 20.6 days, i.e., 4.7 days longer than the CTL run and 3.4 days longer than the E-Folding test. Furthermore, the Two-Tiered scheme outperforms CTL and E-Folding over the western Indian Ocean, but it underperforms over the Maritime Continent. It exhibits poorer eastward propagation characteristics and larger MJO errors due to larger SST forcing errors, which mainly from the analysis-related errors of the coupled model. Larger SST forcing errors increase the water vapor biases and amplify MJO prediction errors, indicating the need for optimized SST forcing. Additionally, the Two-Tiered scheme significantly enhances anomaly correlation coefficient (ACC) skills for 500-hPa geopotential height, with the improvement closely linked to the enhancement in MJO forecast skill. Overall, even the one-way SST forcing that includes multi-faceted information of the coupled model can provide guidance for extended-range prediction with atmospheric-only numerical models.