Ongoing and future climate change may be of sufficient magnitude to significantly impact global forest ecosystems. In order to anticipate the potential range of changes to forests in the future and to better understand the development and state of forest ecosystems at present, a variety of forest ecosystem models of varying complexity have been developed over the past 40 years. While most of these models focus on representing either forest demographics including age and height structure, or forest biogeochemistry including plant physiology and ecosystem carbon cycling, it is increasingly seen as crucial that forest ecosystem models include equally good representations of both. However, only few models currently include detailed representations of both biogeochemistry and demographics, and those mostly have high computational demands. Here, we present TreeM-LPJ, a first step towards a new, computationally efficient forest dynamics model. We combine the height-class scheme of the forest landscape model TreeMig with the biogeochemistry of the dynamic global vegetation model LPJ-GUESS. The resulting model is able to simulate forest growth by considering vertical spatial variability without stochastic functions, considerably reducing computational demand. Discretization errors are kept small by using a numerical algorithm that extrapolates growth success in height, and thereby dynamically updates the state variables of the trees in the different height classes. We demonstrate TreeM-LPJ in an application on a transect in the central Swiss Alps where we show results from the new model compare favorably with the more complex LPJ-GUESS. TreeM-LPJ provides a combination of biological detail and computational efficiency that can serve as a useful basis for large-scale vegetation modeling.