Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness and mitigation of extreme events. Previously thought to peak mainly in winter, recent research reveals that ARs exhibit region-specific seasonality. However, AR analysis is heavily influenced by the chosen detection algorithm. Our study examines how AR seasonality varies based on both location, year and algorithm selection. We investigate the link between year-to-year consistency of peak AR activity and the presence of a dominant seasonal pattern. We categorize regions based on their year-to-year seasonality 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. Additionally, different algorithms may detect a consistent seasonal pattern for the same region but disagree on the exact dominant season. This is exemplified by the conflicting results obtained for China. Integrated Vapor Transport (IVT) often corroborates consistent or inconsistent patterns across regions. In conclusion, this study suggests that variations in the consistency of seasonal patterns are related not only to the detection technique but also to atmospheric circulation, synoptic and low-frequency anomalies. Understanding the variations in the consistency of seasonal pattern in areas like Britain remains challenging due to algorithmic and physical differences. These findings emphasize the need for a multi-faceted approach to AR research, considering not just detection methodologies but also regional characteristics and atmospheric processes. Understanding the specific reasons for inconsistent seasonal patterns is an important next step for future research to improve forecasts and preparedness.