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Stream temperature is an important variable for the aquatic system as it plays a key role for habitats of aquatic species and interacts with multiple other variables such as dissolved oxygen and nutrients. Therefore, it directly and indirectly affects fish, invertebrate communities, primary production and other biological processes. Due to global climate variability, increased stream temperatures are becoming more relevant in ecohydrological research. However, spatial and temporal stream temperature dynamics are impacted by a complex interplay between climate, hydrological processes, and catchment characteristics. In many ecohydrological model applications, this interplay is simplified or neglected. To address these challenges, a more detailed representation of stream temperature and its spatio-temporal variability is required.Our study addresses the limitations of a simplified stream temperature model by using the ecohydrological model SWAT+ to demonstrate how process representation can improve simulations. SWAT+ currently predicts stream temperature based on a relatively simple linear relationship with air temperature. Important factors are not considered, since the influence of runoff components, heat exchange, and riparian shading are neglected in stream temperature predictions. To improve the representation of temperature related processes, we modified the SWAT+ model source code (version 60.5.4) and included mixing of hydrological processes (Ficklin et al., 2012), heat transfer processes (Du et al., 2018), and shading (Noa-Yarasca et al., 2023). The enhanced SWAT+ model was tested at 23 stations in the medium-sized mountainous Kinzig catchment (Central Germany) with high-resolution observed stream temperature data.The enhanced model performed significantly better than the default model, achieving a mean KGE of 0.8 across multiple calibration sites (improved from 0.72 with the default linear model). We investigated, improved, and tested previous advances in stream temperature modelling within this work, highlighting the importance of accurate process representation. Furthermore, our results emphasize the necessity of a good hydrological calibration for a satisfactory stream temperature model performance. The resulting model serves as a valuable tool for ecological research and catchment management. By replacing empirical simplifications with process-based source code modifications, we provide a methodology for improving stream temperature representation that is transferable to other models.