Abstract This paper discusses the performance of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast model in predicting rainfall over the Korean Peninsula caused by tropical cyclones (TCs). TCs forecasted in GFS were characterized by overestimated intensity and a well-organized inner-core structure during landfall, and the associated TC-induced rainfall forecast errors were examined from two perspectives: TC tracks and TC–environmental interactions. The results provide implications in three ways. First, the predicted TC track is biased to the west, bringing it closer to the Korean Peninsula landmass and contributing to an overall increase in overland precipitation. Second, the strongly simulated TC during landfall predicted rainfall only in a narrow radius around the TC center. Third, the TCs were characterized by a weak upper-level TC outflow due to the well-developed TC structure, which could increase the inertial stability of the TC center during jet interaction, leading to an underestimated precipitation in the outer-core region of the storm. These results suggest specific sources of error in the forecast of TC precipitation over the Korean Peninsula, which can contribute to improving numerical models and providing forecasters with information about model characteristics. Significance Statement This study evaluates the performance of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) in forecasting precipitation from tropical cyclones (TCs) over the Korean Peninsula. The GFS model tends to overestimate the intensity and depict a more well-developed structure of these TCs during landfall, leading to three key issues. First, there is a westward bias in TC track prediction, resulting in increased overland rainfall. Second, the model shows a concentrated rainfall distribution around the center of TCs. Third, there is a lack of upper-level outflow in the model, underestimating rainfall in the TC outer-core regions due to poor TC–jet interaction forecasts. These insights highlight specific errors in the model and provide guidance for improving future forecasts.