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Understanding spatial scale-dependent changes in extreme precipitation is crucial for flood risk adaptation in a warming climate. This study investigates how future changes in sub-daily to daily extreme precipitation can be transferred from point to catchment scales. We use the areal reduction factor (ARF) concept to quantify the impact of climate change on extremes at multiple scales from point (individual model grid cell) to catchment scales up to 3,751 km 2 in the upper Adige River basin, eastern Italian Alps. We apply the Simplified Metastatistical Extreme Value (SMEV) framework to estimate extreme precipitation return levels (1–24 h durations, up to 100-year return periods) at multiple spatial scales using an ensemble of nine convection-permitting models from FPS-CORDEX. Models provide hourly precipitation data (remapped to 3 km grid) for historical (1996–2005) and future (2090–2099) periods under the RCP8.5 scenario. Our results show that ARFs derived by the models in the historical period align well with ARFs values obtained from observations. Under future scenarios, changes in ARFs cannot be in general deemed statistically significant, due to the important inter-model variability. ARFs slightly increase for short durations (1–3 h) and low-to-medium return periods (2–20 years) over small basins ( < 100 m a t h r m k m 2 ), suggesting more spatially uniform extremes at these scales. Conversely, ARFs decrease for longer durations, larger basins and high return periods, implying that corresponding areal design precipitation will increase less than point precipitation under the same conditions. Our results highlight the need for high-resolution ensemble modeling to capture inter-model variability in extreme precipitation projections.
Vohnicky et al. (Sat,) studied this question.