Exploring the linkage between dew point temperature and precipitation extremes: A multi-time-scale analysis on a semi-arid Mediterranean region

Understanding warming climate implications on precipitation is of crucial importance, especially for areas particularly subjected to climate changes and land use/cover modifications, which could be extremely vulnerable to phenomena typically caused by rainfall extremes, such as floods and landslides. Past decade has been witnessing an increasing interest on simple modeling approaches based on the observation of commonly available meteorological variables and their physical linkages. In particular, based on the well-known thermodynamic Clausius-Clapeyron (CC) equation, it was widely investigated the scaling relation between rainfall extremes and variables representative of the near surface humidity, typically the surface air temperature, through the use of general regression models. In some cases, conventional approaches have shown some evident limitations related to the use of surface temperature as covariate, limited size of the analyzed datasets and some climatic peculiarities of the investigated areas, especially for tropical, arid and semi-arid environments. The use of quantile regression instead of general regression and dew point temperature instead of surface temperatures have recently revealed promising potentialities for overcoming some of the above limitations for wet areas, while it has been scarcely tested in arid and semi-arid regions. The purpose of this study is to analyze the suitability of a quantile regression-based approach in a semi-arid Mediterranean region and to explore the impact of different modeling choices on the estimation of the scaling rate. More specifically, the sensitivity of extreme precipitation to dew point temperature is investigated through a multi-time-scale analysis, performed on a wide regional dataset of Sicily (Italy) including high temporal resolution climatic data from 86 gauges. The role of the considered rainfall accumulation period, conditional quantile and time-lag between precipitation-dew point temperature paired data is investigated considering different spatial and temporal data aggregation. The results reveal scaling rate values always below the theoretical CC-rate. Hourly and sub-hourly rainfall extremes are more sensitive to changes in dew point temperature than longer precipitation. The analysis of the driest and hottest season shows a dew point temperature dependence of extreme rainfall more complex than for the other seasons. Compared to the use of general regression, the scaling relationship observed with the quantile regression approach, here used, is more regular across different gauges and sub-regions, rainfall accumulation periods and seasons, with similar scaling rates, confirming promising potentialities areas also for semi-arid regions