Toward the stochastic modelling of extreme precipitation probability with thermodynamic and dynamic covariates

To properly adapt to climate change, we need to estimate extreme precipitation probability in future climate scenarios. The task is particularly challenging for sub-daily and sub-hourly extremes, as they are hardly represented by most of the available climate models. As an alternative to explicit model simulations, one can use stochastic models trained on physical covariates. For example, it was recently shown that we can predict changes in sub-daily and sub-hourly extreme precipitation only based on shifts in wet-day daily temperatures. With the aim of extending the applicability of such stochastic models, we examine here the use of covariates representing both thermodynamic and dynamic processes.

We focus on a set of ~300 stations in the Alps (from France, Switzerland, Austria, Italy) for which we have sub-daily precipitation and temperature observations. First, we assess the importance of statistical independence of the events on the identification of the scaling relationships between extreme precipitation and temperature that are commonly used to quantify the thermodynamic component. Then, we evaluate the relative importance of the thermodynamic and dynamic components for durations ranging between 10 minutes and 24 hours using as covariates dew point, vertical velocity at 500 hPa, and divergence at 300 hPa from ERA5 reanalysis simulations.

Our results show that (1) evaluating extreme precipitation-temperature scaling relations using all the wet time intervals (as done in several studies) leads to biased estimates of the scaling rates relevant for extreme sub-daily precipitation projections. (2) The scaling rates between extreme precipitation and dew point tend to decrease logarithmically with duration, an information that can be used to extract the scaling rate at sub-hourly durations from hourly observations. (3) The importance of the thermodynamic component decreases with duration (rank correlation decreases from ~0.55 at 10 minutes to ~0.2 at 24 hours), while the importance of the dynamic component that can be appreciated at the ERA5 resolution (~30 km) tends to increase with duration (rank correlation increases from ~0.2 at 10 minutes to ~0.45 at 24 hours). (4) From a stochastic simulation perspective, temperatures and dew point during precipitation events in the Alps can be simulated using generalized normal distributions (or normal distributions in case of seasonal data), while vertical velocities and divergence need to be simulated using skewed models such as a generalized extreme value distribution.