Abstract: Cloud detection is critical for applications of infrared high-spectral radiance observations as it directly impacts the effectiveness of satellite data utilization. McNally proposed a method in 2003 based on observed and simulated brightness temperature differences for channel cloud detection, widely applied in satellite data quality control for numerical weather forecasting. Building upon this method, this study utilized Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud classification data products to quantitatively assess the cloud detection performance of FengYun 3D (FY-3D) High Spectral Infrared Atmospheric Sounder (HIRAS), using precision and recall as validation metrics. Through this assessment, the assimilated data volume of FY-3D HIRAS products was enhanced. Results revealed the following: (1) The precision of FY-3D/HIRAS channel cloud detection is 97.19%, with a recall of 93.74%. The root mean square error of the difference between the observed brightness temperature and background brightness temperature caused by false clear-sky channels (i.e., cloud channels detected as clear sky) was 0.984 K, which was within observational error variance in numerical forecasting. Thus, the results confirm that the method does not compromise data quality and can be effectively applied to numerical weather forecasting. (2) The CALIPSO-based analysis of different cloud types showed that high precision but lower recall was achieved for stratus, stratocumulus, and fractured cumulus. In contrast, high precision and recall were achieved for altocumulus, altostratus, and deep convective clouds, and lower precision but higher recall was achieved for cirrus.