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RESEARCH PRODUCT

Sensitivity of extreme rainfall to temperature in semi-arid Mediterranean regions

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Abstract Warmer air has the potential to hold more water vapour and, therefore, to provide more water to rainfall events. Studying the relationship between rainfall and temperature represents an emerging issue in hydrology and meteorology, since it can be considered fundamental for evaluating the effects of global warming on future precipitation. Various approaches have been tested across different parts of the world, in many cases observing an intensification of extreme precipitation at higher temperatures consistent with the well-known thermodynamic Clausius-Clapeyron relation (CC-scaling rate of 6–7%°C−1). However, at different locations for hourly time-scales, the temperature-extreme rainfall scaling can be higher (super-CC) or lower (sub-CC). This study contributes to the understanding of the scaling relationship between extreme rainfall and temperature under climate conditions characteristic of Mediterranean semi-arid regions, rarely explored in the past. The role of different factors, such as rainfall characteristics and climatic seasonality, modelling framework and rainfall accumulation period are investigated through an application to Sicily (Italy). In particular, the suitability of different types of regression models used to interpret the relationship between hourly and sub-hourly extreme rainfall and surface temperature is explored. We find overall a sub-CC scaling for most of the island of Sicily. However, the rainfall-temperature scaling relationship is not constant over the temperature range and may be dependent on the season. The different results obtained highlight the importance of modelling choices for analyses in regions characterized by semi-arid climates. More specifically, we observe increasing scaling rates for decreasing rainfall accumulation periods, and significant sensitivity of scaling rates to the selected extreme rainfall quantile. Our novel use of piecewise and locally-weighted scatter plot smoothing regression-based approaches allow the accurate characterization of the temperature dependence of extreme rainfall in Sicily. This identifies a peak-like structure for the drier season, not detected by the simple application of the commonly-used exponential regression based approach.