The data volume produced by regional and global multicomponent Earth system models is rapidly increasing because of the improved spatial and temporal resolution of the model components and the sophistication of the numerical models regarding represented physical processes and their complex non-linear interactions. In particular, very small time steps need to be defined in non-hydrostatic high-resolution modeling applications to represent the evolution of the fast-moving processes such as turbulence, extratropical cyclones, convective lines, jet streams, internal waves, vertical turbulent mixing and surface gravity waves. Consequently, the employed small time steps cause extra computation and disk input-output overhead in the modeling system even if today's most powerful high-performance computing and data storage systems are considered. Analysis of the high volume of data from multiple Earth system model components at different temporal and spatial resolutions also poses a challenging problem to efficiently perform integrated data analysis of the massive amounts of data when relying on the traditional postprocessing methods today. This study mainly aims to explore the feasibility and added value of integrating existing in situ visualization and data analysis methods within the model coupling framework. The objective is to increase interoperability between Earth system multicomponent code and data-processing systems by providing an easy-to-use, efficient, generic and standardized modeling environment. The new data analysis approach enables simultaneous analysis of the vast amount of data produced by multicomponent regional Earth system models during the run-time. The presented methodology also aims to create an integrated modeling environment for analyzing fast-moving processes and their evolution both in time and space to support a better understanding of the underplaying physical mechanisms. The state-of-the-art approach can also be employed to solve common problems in the model development cycle, e.g., designing a new subgrid-scale parameterization that requires inspecting the integrated model behavior at a higher temporal and spatial scale simultaneously and supporting visual debugging of the multicomponent modeling systems, which usually are not facilitated by existing model coupling libraries and modeling systems.