Extreme precipitation events associated with atmospheric rivers (ARs) trigger floods, landslides, and avalanches that threaten lives and livelihoods in Southeast Alaska. Six rural and indigenous communities (Hoonah, Klukwan, Skagway, Yakutat, Craig, and Kasaan) identified specific needs regarding these hazards and joined the Southeast Alaska Coastlines and People (CoPe) Kutí­ Hub to address the shared challenge of understanding and predicting these events. This study presents a climatology (1980–2019) of synoptic, mesoscale, and local meteorological characteristics of ARs and heavy precipitation across this region. High-amplitude upper-level patterns across the northeastern Pacific Ocean favor ARs reaching Southeast Alaska, where moisture is orographically lifted, resulting in heavy precipitation. In the six communities, ARs occur 8–15 days per month, yet only 9 AR days per year account for up to 75–90% of precipitation extremes. Furthermore, 79–95% of days with extreme precipitation have > 75th percentile integrated water vapor transport (IVT), demonstrating the strong relationship between IVT and extreme precipitation. This study also highlights the relationship between IVT direction and complex coastal topography in determining precipitation extremes. For example, in Klukwan and Skagway, 80–90% of extreme ARs have south-southwesterly or south-southeasterly IVT. Coastal communities like Yakutat experience higher IVT and precipitation overall, and although southeasterly IVT is more common, extreme precipitation events are most common with southwesterly IVT. Collaboration with the National Weather Service in Juneau, Alaska will lead to improved situational awareness, forecasts, and Impact Decision Support Services to remote communities, saving lives and property in a region vulnerable to the impacts of climate change.

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth’s hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a pre-industrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.