In meteorological simulations, it is crucial to accurately represent the exchange of momentum, energy, and matter between the surface and the atmosphere. These exchanges ("surface fluxes") are computed through parameterizations, relying on both a theoretical framework (i.e., a set of equations) and a set of physically measured coefficients required in the equations. For example, the friction velocity, which reflects the slowing down of the flow on the surface, is related to the wind in the surface layer and the value of the roughness length (z 0 ), which is a physical characteristic of the surface. Surface energy balance, in which surface albedo and emissivity are driving surface characteristics for the distribution between the radiative terms of the surface energy balance, of which the sensible heat flux is a key component. Another critical component in this balance is the ground heat flux, dependent on the physical characteristics of the vegetation, soil, and soil water content. All these coefficients depend on the surface land cover (LC).Consequently, the representation of LC in models through the appropriate parameters not only has a direct influence on the simulated processes close to the surface (Oke, 2002), but it also affects mesoscale circulation (Hartmann, 2015;Weaver & Avissar, 2001;Yang, 2004), and LC is crucial for correct simulations by numerical weather prediction (NWP) models (Jach et al., 2020). Most models, such as the Weather Research and Forecasting (WRF) model (Skamarock et al., 2019), often accommodate simulations of both small and large scales in a so-called grid-nesting mode for resource-saving and numerical error reduction purposes (Daniels et al., 2016;Wang & Gill, 2012). This means that the fluxes are computed at different resolutions according to the different domains, and the question arises as to how to define an appropriate way to aggregate either the surface parameters or the fluxes to the considered cell size.Two main approaches exist for using the LC information available in finer detail than the resolution of the grid (subgrid variability). The first approach selects a unique representative LC from the available categories for each cell, often through the dominant approach (most common value a.k.a. SLM; Single Level Mode) or the nearest Abstract Land cover (LC) data incorporation, for weather modeling purposes, highlights many problems.The straightforward Single Level Mode (SLM) aggregation is not adapted for high-resolution LC maps, with a high number of classes, because it could generate false classifications. We propose a Multi-Level Mode (MLM) aggregation method that includes a hierarchical structure. This study focuses on the Corine Land Cover (CLC) data set. Differences between MLM and SLM methods are small at the finest horizontal resolution and increase to a value of around 16% at 9-km horizontal resolution. To further integrate CLC data into WRF (Weather Research Forecasting model), we included a dedicated table of physical parameters next to using the classical convers...