Climate Change Effects on Probable Maximum Precipitation (PMP) of Mesoscale Convective Systems: Model-based Estimation and Large Ensemble-based Frequency Analysis

Abstract

Understanding of the effects of climate change on Probable Maximum Precipitation (PMP)-like extreme precipitation is limited but urgently needed to update outdated design values. This study aims to quantify these effects through the application of atmospheric model-based PMP estimation methods to Mesoscale Convective Systems (MCSs). The annual exceedance probability (AEP) of deterministic estimates was assessed using large ensemble climate simulations, which is proposed as a framework for combining deterministic estimations with probabilistic evaluations for model-based PMP estimation. The results revealed that the relative humidity maximization method does not necessarily work for MCSs. This is because it increases the convective stability of the atmosphere, thereby suppressing the organization of the MCSs. The storm transposition method combined with the pseudo-global warming method successfully produced a large number of heavy rainfall scenarios corresponding to various warming patterns. As a result, the warming scaling rate with surface dew point temperatures for PMP-magnitude extreme precipitation (AEPs of 10 −4 –10 −7 ) was found to be >21 %/K (triple Clausius-Clapeyron), with substantial variation across durations and basins. The large ensemble-based frequency analysis revealed that the estimated PMP magnitude become more frequent with AEPs increasing by approximately 10 1 ∼10 2 from the historical to +4 K climate conditions. To the best of our knowledge, this is the first quantification of climate change effects on PMP-like extreme precipitation, explicitly including dynamic impacts, thereby enabling the evaluation of sub-daily durations. More robust estimates of warming scaling rates and AEP changes can be achieved by incorporating a larger number of rainfall events into the PMP estimation process.

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