Mineral dust is a major atmospheric aerosol influencing climate, air quality, and human health through radiative and microphysical processes. The Iberian Peninsula is frequently affected by North African dust intrusions, leading to episodic PM₁₀ exceedances that challenge air quality forecasting. However, accurate representation of dust emissions remains limited by uncertainties in soil erodibility, land surface properties, and meteorological forcing.</p> <p>This study evaluates the impact of two high-resolution soil erodibility datasets on dust simulations using WRF-Chem with the GOCART scheme. The first dataset, EROD-HR, integrates fine-resolution topography (GMTED2010) to improve dust source representation at 0.0625 &deg; (~5 km) globally and 1 km over the Iberian Peninsula. The second dataset, SOILHD, further refines dust source characterization by incorporating high-resolution soil texture (sand, silt, clay fractions) and removing misclassified bare soil areas, reaching 1 km global resolution. Both datasets aim to better capture spatial heterogeneity of dust sources in semi-arid environments.</p> <p>Simulations are conducted for five dust episodes between 2022 and 2025, covering local and long-range transport conditions. Model performance is evaluated against PM₁₀ observations from the SINQLAIR network in the Region of Murcia. Results show improved representation of dust emissions, with better agreement in magnitude and timing of PM₁₀ peaks at inland stations. Improvements are more limited at coastal and anthropogenically influenced sites, although statistical metrics (correlation, bias, RMSE) indicate consistent gains.</p> <p>Overall, high-resolution erodibility datasets enhance WRF-Chem dust simulations by reducing biases and improving variability representation, highlighting the importance of detailed land-surface information for regional dust forecasting systems.