Abstract: Medium-range forecasting experiments were conducted using the 0.125-degree weather forecast model configuration of the domestically developed Global-Regional Integrated Forecast System (GRIST). The precipitation forecast performance of GRIST’s baseline version was evaluated by comparison with the ERA5 reanalysis data, satellite observation data (GPM) and two global numerical weather prediction models. In addition, the sensitivity of GRIST to different dynamic configurations was explored. The results show that GRIST when initiated from a cold start, can simulate global 500-hPa circulation patterns. Its performance in terms of the 500-hPa geopotential height anomaly correlation coefficient (ACC500) is comparable to that of the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP). Regarding precipitation simulation, GRIST successfully captures the spatial distribution of global mean precipitation, aligning overall with observations. However, as integration time increases, GRIST tends to exhibit larger systematic precipitation wet biases in precipitation than NCEP-GFS, particularly over the intertropical convergence zone (ITCZ) and the south slope of the Qinghai–Xizang Plateau. An analysis of precipitation intensity and frequency suggests that these wet biases stem from an overestimation of precipitation frequency. To further explore this, six key regions were selected to investigate the forecasted precipitation intensity–frequency spectrum and its diurnal variation. In these analyses, GRIST more accurately simulated the intensity and frequency structure of “heavy precipitation” compared to NCEP-GFS. The simulation performance of diurnal variation of precipitation is generally reasonable, but an overestimation and advance of precipitation peak was found in several areas. Both the hydrostatic and non-hydrostatic dynamical cores of GRIST are highly consistent in the 0.125-degree resolution weather predictions. Experiments using 60 layers, as opposed to 30 layers, provided added value om simulating circulation and precipitation patterns.