The consideration of gross land changes, meaning all area gains and losses within a pixel or administrative unit (e.g. country), plays an essential role in the estimation of total land changes. Gross land changes affect the magnitude of total land changes, which feeds back to the attribution of biogeochemical and biophysical processes related to climate change in Earth system models. Global empirical studies on gross land changes are currently lacking. Whilst the relevance of gross changes for global change has been indicated in the literature, it is not accounted for in future land change scenarios. In this study, we extract gross and net land change dynamics from large-scale and high-resolution (30-100ĝ€m) remote sensing products to create a new global gross and net change dataset. Subsequently, we developed an approach to integrate our empirically derived gross and net changes with the results of future simulation models by accounting for the gross and net change addressed by the land use model and the gross and net change that is below the resolution of modelling. Based on our empirical data, we found that gross land change within 0.5° grid cells was substantially larger than net changes in all parts of the world. As 0.5° grid cells are a standard resolution of Earth system models, this leads to an underestimation of the amount of change. This finding contradicts earlier studies, which assumed gross land changes to appear in shifting cultivation areas only. Applied in a future scenario, the consideration of gross land changes led to approximately 50ĝ€% more land changes globally compared to a net land change representation. Gross land changes were most important in heterogeneous land systems with multiple land uses (e.g. shifting cultivation, smallholder farming, and agro-forestry systems). Moreover, the importance of gross changes decreased over time due to further polarization and intensification of land use. Our results serve as an empirical database for land change dynamics that can be applied in Earth system models and integrated assessment models.