Atmospheric deposition refers to the transfer of pollutants from the atmosphere into the environment using either wet or dry deposition. The significance of monitoring the seasonality of atmospheric deposition cannot be overstated because it has direct implications on human health, crop yield, and aquatic environments. While the seasonality of deposition events has been widely reported for decades, their persistence over time, consistency in terms of both wet and dry deposition, and responses under non-stationary processes have not yet been fully quantified. In this study, we developed an automated scalable analysis approach with no stationarity assumptions, employing strict data quality control procedures, Box-Cox transformation based on segments, and frequency (FFT) and autocorrelation function (ACF) based approaches, applied to 128 high-quality data segments of the major ions across the USA measured by CASTNET and NADP/NTN. The developed approach allowed us to conduct a rigorous analysis comparing FFT with ACF, showing persistent annual and semi-annual cycles for a wide range of chemical species. Additionally, it was possible to detect fine phase differences between sites based on changes in the lag window used in ACF analysis, as well as map these cycles geographically through combination of ACF lags for different sites, detecting transport-driven seasonality in the Southeast and Midwest regions. Thus, it was shown that taking seasonality into account can significantly reduce biases in long-term trend detection, critical loads, and modeling of wet and dry deposition totals. The study demonstrates the importance of accounting for these cycles to diminish biases when assessing long-term trends, policy effects, and models of total deposition. All code used is available on GitHub.