Long-term trend analysis and modeling of aerosol distribution is of paramount importance to study radiative forcing, climate change, and human health. Previous studies on spatiotemporal trend analysis have not fully considered the impact of spatial and temporal gaps of satellite-retrieved aerosol optical depth (AOD) on precise aerosol characterization. In addition, very few studies analyzed inter-country aerosol variations, trends, driving forces, and predictions at the regional level, which is important to draw lessons from the experiences of one another. This study is focused on comparative time series analyses and modeling of aerosols over the contiguous United States (U.S.) and China during 2003-2015 using MODIS Collection 6 retrievals. An econometric model, namely autoregressive integrated moving average (ARIMA), is employed to reproduce and predict AOD variability over U.S. and China. Results show that high AOD values are observed in the eastern part of U.S. and China. Temporal variations indicate that AODs reach their peak values in summer for both countries. A sustained negative AOD trend is present throughout the U.S. while a distinct spatial variation of AOD trend is exhibited in China. The large differences in variations and trends are closely linked to the energy strategies, economic and urban development, and lifestyle activities of these two countries. Time series modeling reveals that reasonably good performances are found in most parts of these two countries. In particular, the model replicates AOD time series that has clear seasonal variations with much more accuracy. The results suggest that areas most suitable for applying the model for prediction are those with high AOD quality, high completeness of AOD data, and low-AOD values. Overall, the satisfactory predicted results indicate the applicability and feasibility of the ARIMA modeling technique for accurately extracting AOD profiles, predicting future AOD values as well as extrapolating missed AOD values at the regional scale. The retrieved and predicted AOD values may serve as reliable estimates for air quality and epidemiological studies.