Developing a radiation emulator based on machine learning in a weather forecasting model is valuable because it can significantly improve the computational speed of forecasting severe weather events. In order to fully replace the radiation parameterization in the weather forecasting model, the universal applicability of radiation emulator is essential, indicating a transition from the research to the operational level. This study addressed the universal issue of radiation emulators associated with horizontal resolutions from the climate simulation scale (100 km) to the cloud-resolving scale (0.25 km). All simulations were performed using an emulator trained at 5 km simulation. In real-case simulations (100–5 km), the forecast errors of radiative fluxes and precipitation were reduced at coarse resolutions. The ideal-case simulations (5–0.25 km) also showed a similar feature with increased errors in heating rates and fluxes at fine resolutions. However, all simulations maintained an appropriate accuracy range compared with observations in real-case simulations or the infrequent use of radiation parameterization in ideal-case simulations. These findings demonstrate the feasibility of a universal radiation emulator associated with different resolutions and models and emphasize the importance of future development directions toward the emulation of high-resolution modeling.