Atmospheric rivers are common precursors but poor predictors of precipitation-induced landslides in western North America

Atmospheric rivers (ARs) are long, narrow, and transient corridors of intense water vapor transport in the lower atmosphere. By driving precipitation in the mid-latitudes, ARs sustain freshwater supply but also cause precipitation-induced disasters such as floods and landslides. In western North America, where precipitation-induced landslides (PILs) are a major geological hazard, ARs have been identified as key drivers of the precipitation regime and frequent precursors of landslide activity. Yet their value as predictors of PILs remains unknown.

In this study, we assess whether AR conditions can inform landslide early-warning efforts in western North America. We employ PIKART—a state-of-the-art AR catalogue at 0.25° and 6-hourly resolution—and a compilation of landslide catalogues across the region—the USGS Landslide Inventories Across the United States, the NASA Cooperative Open Online Landslide Repository, and the Preliminary Canadian Landslide Database—to investigate the association between ARs and PILs from 1996 to 2018. Based on their intensity and persistence, we classify ARs on an AR-strength scale from AR1 to AR5 and analyze how PIL occurrence varies across these strength ranks.

We find that AR conditions preceded more than 80% of days with reported PILs along the West Coast, yet most landslides were associated with weak, primarily beneficial ARs. Both isolated ARs and multi-event AR families contributed comparably to PIL occurrence. Despite this high co-occurrence, ARs exhibit little predictive power because most ARs do not trigger landslides: forecast skill is below 4% across most landslide-prone locations and does not exceed 15% even in regions with dense reporting, such as Portland, Oregon. Although neither the most frequent nor the most hazardous, moderate ARs of rank AR3 show the highest predictive skill. These results reveal a fundamental disconnect between the prevalence of ARs before landslides and their ability to predict them, highlighting both the challenges and opportunities of AR-based landslide forecasting in western North America.