LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2018, http://doi.org/10.5194/gmd-2017-145). We here present an extended version of the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). Additional features of this version, including agricultural trees as a new cultivation type in LPJmL4, are described and used in Jans et al. (2020, HESS) The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. The model data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2018 part I). Additionally, these results include agricultural trees (olives, non-citrus orchards, and cotton) implemented as a new cultivation type into LPJmL4. Standard results are evaluated in Schaphoff et al. (2018 part II). Results of cotton as a newly implemented agricultural tree are evaluated in Jans et al. (2020), HESSD. The data collection includes some key output variables made with the model setup described by Jans et al. (2020, HESS). Overall, data sets are resulting from 40 different simulations, where we combined 5 different GCMs (GFDL, HadGEM, IPSL, MIROC, NorESM) with 4 different RCPs (2p6, 4p5, 6p0, 8p5) without and with CO2 fertilization, respectively. The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for crop yields, absorbed photosynthetically active radiation and the water fluxes (irrigation, transpiration, evaporation,interception, blue and green evapotranspiration). Crop yields, and water fluxes are given for each crop functional type (CFT), respectively. Monthly data are provided for one carbon flux (net primary production) and the water fluxes transpiration, evaporation, interception, and runoff. The data are provided in one binary file for each variable and simulation. An overview of all variables and information on how data are stored within the binary files are given in the file inventory.