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.

The megaflood in the Jing-Jin-Ji region of China at the end of July 2023, driven by two typhoons and orographic precipitation, was a major disaster. In this study we show that the extreme rainfall event is well reproduced using the 3.25 km and 800 m Regionally Refined Mesh (RRM) configuration of the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). A projected 5.8\%/degC increase in Jing-Jin-Ji precipitation under 2.16 degC warming by 2050 for this event is related to markedly increases in condensation rate and vertical velocity. Free-running simulations further show that the response of the mesoscale circulation to warming results in more pronounced local precipitation changes and shifts in rainfall patterns. The value of SCREAM for assessing the impact of climate change on extreme events and the importance of high-resolution climate modeling are emphasized.