Abstract: Based on the tropical regional atmospheric model system of the China Meteorological Administration (CMA-TRAMS) and the European Centre for Medium-Range Weather Forecasts (ECMWF), the forecast differences of tropical cyclone (TC) formation for the No. 3 Typhoon “Chaba” in 2022 are compared and analyzed. The reasons for the successful genesis prediction by CMA-TRAMS for Typhoon “Chaba” are investigated from different aspects, such as the development environment of tropical cyclone (TC) embryos, physical processes within TC embryos, and embryo structure and the corresponding development. The results indicate that numerical models are required to have a good descriptive ability in terms of the aforementioned aspects to perform well in TC generation forecasts. This study is beneficial for understanding the main physical processes and factors closely related to TC generation prediction in numerical models. It also provides clues for subsequent model development and improvement. The results indicate that a significant difference in the cyclonic circulation pattern of the monsoon trough in the western Philippines between the 72 h and 120 h forecasts is the direct cause of the difference in TC generation forecasts between the two models. CMA-TRAMS predicts the generation, development, and merging of multiple mesoscale convective systems (MCSs) or mesoscale convective vortices (MCVs) in a positive vorticity environment from 96 h to 120 h. It also predicts the organization of circulation to form warm core structures, which is important in its successful prediction of the formation of “Chaba.” The accuracy of wind field forecasting in the western monsoon trough of the Philippines may have a significant impact on the prediction of TC generation in the South China Sea. The continuous convergence and merging of MCSs and MCVs, as well as the organization of cyclone circulation, are important physical processes for TC formation. The results of this study have deepened our understanding regarding the main physical processes involved in TC formation, enhanced our knowledge of the influencing factors of numerical model prediction for TC formation, and provided clues for improving model forecasts.