This study investigated a method for creating a climatological dataset with improved reproducibility and reliability for the Southern Ocean. Despite sparse observational sampling, the Southern Ocean has a dominant physical characteristic of a strong topographic constraint formed under weak stratification and strong Coriolis effect. To increase the fidelity of gridded data, the topographic constraint is incorporated into the interpolation method, the weighting function of which includes a contribution from bottom depth differences and horizontal distances. Spatial variability of physical properties was also analyzed to estimate a realistic decorrelation scale for horizontal distance and bottom depth differences using hydrographic datasets. A new gridded dataset, the topographic constraint incorporated (TCI), was then developed for temperature, salinity, and dissolved oxygen, using the newly derived weighting function and decorrelation scales. The root-meansquare (RMS) of the difference between the interpolated values and the neighboring observed values (RMS difference) was compared among available gridded datasets. That the RMS differences are smaller for the TCI than for the previous datasets by 12%–21% and 8%–20% for potential temperature and salinity, respectively, demonstrates the effectiveness of incorporating the topographic constraint and realistic decorrelation scales. Furthermore, a comparison of decorrelation scales and an analysis of interpolation error suggests that the decorrelation scales adopted in previous gridded datasets are 2 times or more larger than realistic scales and that the overestimation would increase the interpolation error. The interpolation method proposed in this study can be applied to other high-latitude oceans, which are weakly stratified but undersampled.