Convection-permitting climate models (CPMs) provide more reliable projections of extreme rainfall compared to regional climate models (RCMs). This study uses a CPM ensemble from the FPS Convection project to assess changes in extreme precipitation over the greater Alpine region. The CPM ensemble shows a stronger increase in extreme precipitation events during summer than RCMs. It also significantly reduces model uncertainty, with more than 50% less uncertainty compared to RCMs. The CPMs better represent local dynamical processes, leading to more accurate local projections of future changes. RCMs often underestimate extreme precipitation, affecting the reliability of climate projections, especially for sub-daily rainfall. This is linked to the parameterization of convection, a key process in extreme weather events. CPMs, which explicitly represent convection, can reduce this uncertainty. Previous studies have shown that CPMs provide more realistic sub-daily statistics and extreme events than RCMs. The study evaluates uncertainties in climate projections, including model uncertainty, natural variability, and climate scenarios. The CPM ensemble shows reduced model uncertainty compared to RCMs, especially for extreme precipitation. The contribution of model uncertainty to total uncertainty is reduced in CPMs, leading to more confident projections. This is particularly important for policymakers planning adaptation strategies. The study uses 9 CPM simulations and their corresponding RCMs to analyze future changes in extreme hourly precipitation. The CPM ensemble shows a significant decrease in low to medium precipitation intensities and an increase in high intensities, consistent with previous findings. The CPMs also show a reduction in model uncertainty, especially for extreme events. The results are consistent across different model subsets, indicating that the findings are not model-specific. The study concludes that CPMs provide more reliable projections of extreme precipitation due to their more realistic representation of local dynamical processes. This is crucial for effective adaptation strategies, as it allows for more certain projections of future changes in extreme rainfall. The findings highlight the importance of using CPMs for climate projections, especially in regions where extreme precipitation events are significant.Convection-permitting climate models (CPMs) provide more reliable projections of extreme rainfall compared to regional climate models (RCMs). This study uses a CPM ensemble from the FPS Convection project to assess changes in extreme precipitation over the greater Alpine region. The CPM ensemble shows a stronger increase in extreme precipitation events during summer than RCMs. It also significantly reduces model uncertainty, with more than 50% less uncertainty compared to RCMs. The CPMs better represent local dynamical processes, leading to more accurate local projections of future changes. RCMs often underestimate extreme precipitation, affecting the reliability of climate projections, especially for sub-daily rainfall. This is linked to the parameterization of convection, a key process in extreme weather events. CPMs, which explicitly represent convection, can reduce this uncertainty. Previous studies have shown that CPMs provide more realistic sub-daily statistics and extreme events than RCMs. The study evaluates uncertainties in climate projections, including model uncertainty, natural variability, and climate scenarios. The CPM ensemble shows reduced model uncertainty compared to RCMs, especially for extreme precipitation. The contribution of model uncertainty to total uncertainty is reduced in CPMs, leading to more confident projections. This is particularly important for policymakers planning adaptation strategies. The study uses 9 CPM simulations and their corresponding RCMs to analyze future changes in extreme hourly precipitation. The CPM ensemble shows a significant decrease in low to medium precipitation intensities and an increase in high intensities, consistent with previous findings. The CPMs also show a reduction in model uncertainty, especially for extreme events. The results are consistent across different model subsets, indicating that the findings are not model-specific. The study concludes that CPMs provide more reliable projections of extreme precipitation due to their more realistic representation of local dynamical processes. This is crucial for effective adaptation strategies, as it allows for more certain projections of future changes in extreme rainfall. The findings highlight the importance of using CPMs for climate projections, especially in regions where extreme precipitation events are significant.