Regional climate modeling is essential for providing reliable information to understand localized impacts and guide adaptation strategies in the context of climate change. This study validates long-term continuous climate simulations over Belgium performed with the ALARO1-SFX model, a novel version of ALARO-1 that incorporates an advanced surface scheme (SURFEX) and improved physiographic datasets.</p> <p>A scale-selective validation setup is introduced, employing a multi-level dynamical downscaling framework to assess the progressive added value of increasing resolution from the mesoscale to convection-permitting scales. The model's performance is evaluated against a gridded observational dataset and precipitation station measurements, focusing on temperature and precipitation biases, diurnal precipitation cycles, and extreme precipitation statistics.</p> <p>Results indicate that higher resolutions (12.5 km and 4 km), combined with the integration of SURFEX into ALARO1-SFX, improve temperature and precipitation biases relative to the 25 km simulation, with the 4 km resolution providing the best representation of hourly extreme precipitation and its diurnal cycle. However, all simulations exhibit varying degrees of wet and cold biases. The findings underscore the added value of convection-permitting modelling for resolving extreme precipitation events and improving diurnal precipitation cycles. Furthermore, the study provides experimental evidence that increasing model resolution adds value for simulating extreme precipitation even when employing a scale-aware deep convection parameterization.