Abstract. Temperate semi-natural grassland plant communities are expected to shift under global change, mainly due to land use and climate change. However, the interaction of different drivers on diversity and the influence of diversity on the provision of ecosystem services are not fully understood. To synthesise the knowledge on grassland dynamics and to be able to predict community shifts under different land use and climate change scenarios, we developed the GrasslandTraitSim.jl model. In contrast to previously published grassland models, we link morphological plant traits to species-specific processes via transfer functions, thus avoiding a large number of species-specific parameters that are difficult to measure and calibrate. This allows any number of species to be simulated based on a list of commonly measured traits: specific leaf area, maximum height, leaf nitrogen per leaf mass, leaf biomass per plant biomass, above-ground biomass per plant biomass, root surface area per below-ground biomass, and arbuscular mycorrhizal colonisation rate. For each species, the dynamics of the above- and below-ground biomass and its height are simulated with a daily time step. While the soil water content is simulated dynamically, the nutrient dynamics are kept simple, assuming that the nutrient availability depends on total soil nitrogen and the total plant biomass. We present a model description, which is complemented by online documentation with tutorials, flowcharts, and interactive graphics, and calibrate the model to grassland sites with different number of mowing events and grazing intensity in central Germany. Furthermore, we show how the model can be used to conduct simulation experiments to analyse shifts in plant community composition under different land use intensities. We believe that the GrasslandTraitSim.jl model is a useful tool for predicting plant biomass production and plant functional composition of temperate grasslands in response to management under climate change.