Abstract: For describing uncertainties in the subgrid-scale energy upscaling transfer, a Stochastic Kinetic Energy Backscatter (SKEB) scheme has been introduced into the Global/Regional Assimilation and Prediction System (GRAPES) global ensemble prediction system (GEPS) to represent model errors more reasonably and increase the ensemble spread. In this research, the SKEB scheme employs the stochastic patterns with temporally and spatially correlated characteristics along with the estimated local kinetic energy dissipation rates caused by numerical diffusion to construct the stochastic stream function forcing. According to the relationship between the streamfunction and the rotational component of horizontal wind, the streamfunction forcing in the SKEB scheme is then transformed into horizontal wind perturbations, which are suitable for the GRAPES global model. The results indicate that, on the one hand, the application of the SKEB scheme improves the simulations of the atmospheric kinetic-energy spectra in the GRAPES model; on the other hand, it leads to a better spread-error relationship, increases the spread of the ensemble and reduces the root mean square error of the ensemble mean to some extent and the improvement is the most pronounced in the tropics. This scheme also contributes to a significant improvement of the continuous rank probability score (CRPS) in the tropics. In terms of precipitation forecast, the results from the Brier score and Area under the Relative Operating Characteristics (AROC) show that the SKEB scheme helps to improve probabilistic forecast skills of rainfall in China for light rain0.1 mm, 10 mm), moderate rain10 mm, 25 mm) and heavy rain25 mm, 50 mm); however, it has little impact on the forecast of rainstorm50 mm, ∞) (24 h precipitation). On the whole, the introduction of the SKEB scheme ameliorates the probabilistic prediction skills of the GRAPES-GEPS.