Redefining Clausius-Clapeyron Scaling to Disentangle Local Thermodynamic vs Large-scale Circulation Controls on Extreme Precipitation

Many Clausius-Clapeyron (CC) scaling studies relate warming to changes in a single precipitation quantile, which can obscure how “extremes” are defined and overlook the fact that events of different rarity are expected to scale at different, yet physically related, rates. CC analyses are also commonly conducted separately for different event durations, limiting insight into whether distinct processes control precipitation variability across timescales. To overcome this limitation, we propose a framework in which the full precipitation-intensity probability distribution is allowed to vary with climate conditions, enabling multiple quantiles to respond differently and to be associated with different drivers.

We apply this approach to observations from 605 stations across the continental United States, exploring how the parameters of hourly and daily precipitation distributions vary with local thermodynamic covariates and indicators of large-scale atmospheric circulation. An additional set of 456 stations with dew point temperature data is used to further assess the role of atmospheric moisture. Stations are grouped by Köppen-Geiger climate zones to ensure robust and coherent relationships. Results show that at the hourly scale, changes in extremes are primarily explained by local temperature and atmospheric moisture availability, with distributional tail thickening under warmer and moister conditions leading to increasingly rapid intensification for rarer events. At the daily scale, controls shift toward non-local influences associated with large-scale circulation. By characterizing scaling behavior across the entire distribution, this framework provides a physically grounded view of how warming affects both typical precipitation and extremes, and highlights the limitations of CC-based approaches.

Our results suggest that the assessment of future extremes should fully account and  resolve the physical processes, such as convection and orographic forcings, responsible for extreme rainfall generation rather than rely on simplistic CC-based methodologies.