Global and zonal monthly means of cloud cover fraction for total cloudiness (CF) from the ISCCP D2 dataset are compared to same quantity produced by the 20th century simulations of 21 climate models from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset archived by the Program for Climate Model Diagnosis and Intercomparison (PCMDI). The comparison spans the time frame from January 1984 to December 1999 and the global and zonal average of CF are studied. The restriction to total cloudiness depends on the output of some models that does not include the 3D cloud structure. It is shown that the global mean of CF for the PCMDI/CMIP3 models, averaged over the whole period, exhibits a considerable variance and generally underestimates the ISCCP value. Very large discrepancies among models, and between models and observations, are found in the polar areas, where both models and satellite observations are less reliable, and especially near Antarctica. For this reason the zonal analysis is focused over the 60° S–60° N latitudinal belt, which includes the tropical area and mid latitudes. The two hemispheres are analyzed separately to show the variation of the amplitude of the seasonal cycle. Most models overestimate the yearly averaged values of CF over all of the analysed areas, while differences emerge in their ability to capture the amplitude of the seasonal cycle. The models represent, in a qualitatively correct way, the magnitude and the weak sign of the seasonal cycle over the whole geographical domain, but overestimate the strength of the signal in the tropical areas and at mid-latitudes, when taken separately. The interannual variability of the two yearly averages and of the amplitude of the seasonal cycle is greatly underestimated by all models in each area analysed. This work shows that the climate models have an heterogeneous behaviour in simulating the CF over different areas of the Globe, with a very wide span both with observed CF and among themselves. Some models agree quite well with the observations in one or more of the metrics employed in this analysis, but not a single model has a statistically significant agreement with the observational datasets on yearly averaged values of CF and on the amplitude of the seasonal cycle over all analysed areas.