Knowledge of cloud properties and their vertical structure is important for meteorological studies due to their impact on both the Earth’s radiation budget and adiabatic heating within the atmosphere. The objective of this study is to evaluate bulk cloud properties and vertical distribution simulated by the US National Oceanic and Atmospheric Administration National Centers for Environmental Prediction Global Forecast System (GFS) using three global satellite products. Cloud variables evaluated include the occurrence and fraction of clouds in up to three layers, cloud optical depth, liquid water path, and ice water path. Cloud vertical structure data are retrieved from both active (CloudSat/CALIPSO) and passive sensors and are subsequently compared with GFS model results. In general, the GFS model captures the spatial patterns of hydrometeors reasonably well and follows the general features seen in satellite measurements, but large discrepancies exist in low-level cloud properties. More boundary layer clouds over the interior continents were generated by the GFS model whereas satellite retrievals showed more low-level clouds over oceans. Although the frequencies of global multi-layer clouds from observations This paper is a contribution to the Topical Collection on Climate Forecast System Version 2 (CFSv2). CFSv2 is a coupled global climate model and was implemented by National Centers for Environmental Prediction (NCEP) in seasonal forecasting operations in March 2011. This Topical Collection is coordinated by Jin Huang, Arun Kumar, Jim Kinter and Annarita Mariotti. H. Yoo Á Z. Li Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20740, USA Z. Li (&) State Key Laboratory of Earth Surface Processes and Resource Ecology, GCESS, Beijing Normal University, Beijing 100875, China e-mail: zli@atmos.umd.edu are similar to those from the model, latitudinal variations show discrepancies in terms of structure and pattern. The modeled cloud optical depth over storm track region and subtropical region is less than that from the passive sensor and is overestimated for deep convective clouds. The distributions of ice water path (IWP) agree better with satellite observations than do liquid water path (LWP) distributions. Discrepancies in LWP/IWP distributions between observations and the model are attributed to differences in cloud water mixing ratio and mean relative humidity fields, which are major control variables determining the formation of clouds.