The application of regression kriging to air pollutants in Japan was examined for the purpose of providing a practical method to obtain a spatial distribution with sufficient accuracy and a high spatial resolution of 1 x 1 km. We used regulatory air monitoring data from the years 2009 and 2010. Predictor variables at 1 x 1 km resolution were prepared from various datasets to perform regression kriging. The prediction performance was assessed by indicators, including root mean squared error (RMSE) and R-2, calculated from the leave-one-out cross validation results, and was compared to the results obtained from a linear regression method, often referred to as land use regression (LUR). Regression kriging well-explained the spatial variability of NO2, with R-2 values of 0.77 and 0.78. Ozone (O-3) was moderately explained, with R-2 values of 0.52 and 0.66. The reason for this difference in performance between NO2 and O-3 might be the characteristics of these pollutants - primary or secondary. Regression kriging outperformed the linear regression method with regard to RMSE and R-2. The performance of regression kriging in this work was comparable to that found in previous studies. The results indicate that regression kriging is a practical procedure that can be applied for the prediction of the spatial distribution of air pollutants in Japan, with sufficient accuracy and a high spatial resolution.