Hybrid-JSBACH4 : Hybrid-Modeling of Land-Atmosphere Fluxes Using Integrated Machine Learning in the ICON-ESM Modeling Framework Description This repositories contains the code for the paper: *ElGhawi, R., Reimers, C., Schnur, R., Reichstein, M., Körner, M., Carvalhais, N., and Winkler, A. J.: Hybrid-Modeling of Land-Atmosphere Fluxes Using Machine Learning integrated in the ICON-ESM Modeling Framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4000, https://doi.org/10.5194/egusphere-egu25-4000, 2025. The repositories are linked to git for the standalone Hybrid-JSBACH4 model: https://github.com/relghawi/Hybrid-JSBACH.gitFor the Hybrid-JSBACH4 parametrizations in ICON-JSBACH4 : https://gitlab.dkrz.de/icon/icon-mpim.git and checking out branch Hybrid-JSBACH4 We share the code for transparency and to demonstrate the concept of hybrid modeling. However, the code is tweaked to our environment and data infrastructure, it cannot be run without adaptions.