Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness and mitigation of extreme events. Previously thought to peak in winter, recent research reveals that ARs exhibit region-specific seasonality and are heavily influenced by the chosen detection algorithm. Our study examines the link between year-to-year consistency of peak AR activity and the presence of a dominant seasonal pattern based on location and the choice of algorithm. We categorize regions based on their year-to-year seasonal characteristics, including consistent patterns (e.g., India, Central Asia), patterns with occasional outliers (e.g., British Columbia coast, Gulf of Alaska), and regions lacking a clear dominant season of peak AR frequency (e.g., South Atlantic, parts of Australia). Hence, not all regions exhibit a consistent seasonal cycle of AR activity. Decision-makers struggle to identify anomalous AR activity within the seasonal to sub-seasonal time scale. This study addresses this challenge by identifying regions with typically inconsistent AR activity and quantifying the extent to which a region has a predictable peak season of AR activity. With algorithms influencing the peak season of AR activity, we also look at two diagnostic variables representative of moisture transport to corroborate the results. We use Integrated Vapor Transport, encompassing both meridional and zonal moisture transport, and Moist Wave Activity, analogous to the zonal variance of Integrated Water Vapor and representing moisture intrusions from lower to higher latitudes. The analysis using these variables shows that inconsistencies in the seasonal cycle of AR activity are not solely due to detection algorithm discrepancies but also due to changes in moisture transport. Understanding how different tropical modes of variability contribute to these inconsistencies is an important next step for future research to improve forecasts and preparedness. For more details please refer to the following ESS Open archive preprint: DOI: 10.22541/essoar.171900796.61669939/v1 .