Abstract: This study systematically describes the detection principle, raw data processing workflow, and quality control methods of the FCDP (Fast Cloud Droplet Probe), a type of airborne forward scattering cloud droplet spectrometer. The performance of each quality control procedure is analyzed and validated using in-situ observational data. To address biases in retrieved parameters including particle number concentration, particle size distribution, and liquid water content that arise from shattered particles, particles in non-valid sampling regions, and coincident particle events in raw FCDP data, a standardized quality control framework is established. The results show that the adaptive method is more applicable and reliable than the aggressive fixed-threshold method for removing shattered particles. The depth of field (DOF) criterion can effectively filter particles outside the qualified sampling regions. The DOF threshold should be selected according to the calibration report of the specific instrument used, so as to achieve a suitable balance between retrieval accuracy and sample representativeness. The method for coincident particle rejection should be selected according to cloud conditions. The symmetric discrimination method based on peak position is suitable for low-concentration clouds owing to its low false rejection rate, whereas the ideal transit-time matching method yields a higher correct rejection rate but also a relatively higher false rejection rate, making it more appropriate for high-concentration clouds. Moreover, the Gaussian beam model is superior to the Top-Hat beam model. Correction based on live time effectively alleviates the overestimation of sampling volume and the underestimation of number concentration and liquid water content in clouds. The results of this study can provide a reference for the standardized processing of observational data from the FCDP and similar forward-scattering cloud particle spectrometers.