We performed an intensive comparison of an isotope-incorporated atmospheric general circulation model with vapor isotopologue ratio observation data by two quasi-global satellite sensors in preparation for data assimilation of water isotope ratios. A global Isotope-incorporated Global Spectral Model simulation nudged toward the reanalysis wind field, atmospheric total column data from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) on Envisat, and midtropospheric (800 to 500 hPa) data from Tropospheric Emission Spectrometer (TES) on Aura were used. For the mean climatological δD of both the total atmospheric column and the midtroposphere layer, the model reproduced their geographical variabilities quite well. There is, however, some degree of underestimation of the latitudinal gradient (higher δD in the tropics and lower δD in midlatitudes) compared to the SCIAMACHY data, whereas there is generally less disagreement except lower δD over the Maritime Continent compared to the TES data. It was also found that the two satellite products have different relationships between water vapor amount and isotopic composition. Particularly, atmospheric column mean δD, which is dominated by lower-tropospheric vapor, closely follows the fractionation pattern of a typical Rayleigh-type "rain out" process, whereas in the midtroposphere the relationship between isotopic composition and vapor amount is affected by a "mixing" process. This feature is not reproduced by the model, where the relationships between δD and the vapor are similar to each other for the atmospheric column and midtroposphere. Comparing on a shorter time scale, it becomes clear that the data situation for future data assimilation for total column δD is most favorable for tropical and subtropical desert areas (i.e., Sahel, southern Africa, mideastern Asia, Gobi, Australia, and the southwest United States), whereas the available midtropospheric δD observations cover wider regions, particularly over tropical to subtropical oceans.