Cloud is an important internal factor of the climate system,especially in the earthatmosphere system. The formation of clouds and their characteristics mainly result from both dynamic and thermodynamic processes of the surface and the atmosphere. An accurate grasp of the distribution of cloud and its variability can contribute greatly when attempting to assess the role of cloud in climate change. And related to this,a calculation scheme that is effective at describing cloudiness is a key part of improving the ability to simulate cloud in numerical models. In order to identify a satisfactory cloudiness calculation scheme, the present study employed NCEP reanalysis data to calculate cloudiness and relative humidity,based on three schemes(Slingo,NCAR,and Qian) ,during a Mei-yu rainfall process in the Yangtze River basin in 2010. Comparative analysis between the calculation results and Local Analysis and Prediction System(LAPS) reanalysis data,considered as the cloud observation,was conducted in terms of cloud distribution and cloudiness values at upper and lower levels, represented by 400 hPa and 850 hPa, respectively. Based on the comparison results,as well as statistical analysis involving anomaly correlation coefficients(ACCs) and RMSE, it was found that the three cloudiness calculation schemes all managed to successfully simulate the cloud central positions,but each had their own advantages and particular characteristics when it came to cloudiness values. The results calculated using the NCAR scheme matched the LAPS outputs very well at largevalue centers of cloud,but the regions containing these values were always too large. To a certain extent,although it was found that the Slingo scheme could also describe the cloud well, it showed a slightly lower capacity than the NCAR scheme in terms of its cloudiness calculation. Additionally, the Qian scheme demonstrated fairly limited ability to calculate the cloudiness values,but always presented the cloud profile and its distribution accurately. Based on the statistical analysis,at 850 hPa, the NCAR scheme produced its maximum ACC and minimum RMSE, indicating its superiority over the other schemes at calculating the cloudiness at that height. However, the Qian scheme yielded the best statistical results at 400 hPa,possibly due to the close correspondence of its cloudiness distribution results with the observation. Of importance here is that, to a certain extent, the Qian scheme takes into account the cloud formation mechanism and the influence of atmospheric vertical motion on cloud formation when calculating the cloudiness. Overall,based on this comprehensive comparison of relevant factors,we conclude that the NCAR scheme is superior to the others,particularly at the lower level(850 hPa).