<p>Enhancing agricultural productivity requires a thorough assessment of fungal-induced crop diseases under varying climatic conditions. We implemented infection risk response functions into a coupled land surface-crop model (Noah-MP-Gecros) for both winter wheat and maize. For wheat, the infection risk module covers Septoria tritici blotch (<i>Zymoseptoria tritici</i>), brown rust (<i>Puccinia triticina</i>), yellow rust (<i>Puccinia striiformis</i>), and Fusarium head blight (<i>Fusarium spp.</i>,<i> mainly F. graminearum</i>). For maize, the focus is on Fusarium ear and stalk rot (primarily <i>Fusarium verticillioides</i>) and Gibberella ear rot (<i>Fusarium graminearum)</i>. We evaluated model performance by simulating infection risks for major wheat and maize diseases across three growing between 2020 and 2023, analyzing infection dynamics and the sensitivity of crop-specific parameters. Model outputs were partially evaluated against field observations. Simulated pathogen infection varied significantly from year to year within the same region. Rust diseases in wheat and Fusarium ear rot and stalk rot in maize displayed similar interannual trends, while Fusarium head blight in wheat, and Gibberella ear rot in maize exhibited divergent patterns across growing seasons. The maximum amount of water intercepted by the canopy and the land-surface parameter momentum roughness length were identified as the most sensitive crop parameters for infection risk. The model, which relies exclusively on meteorological variables, has shown potential for real-time disease risk prediction and guiding pesticide application. The model performance could be improved by integrating biological processes such as host-pathogen interactions and spore availability. On the crop side, pathogen impacts could potentially be mitigated by management and cultivar traits influencing canopy water interception and aerodynamic roughness.</p>