Current forecast models insufficiently represent the complex interactions of aerosols, clouds and precipitation. Simulations with spectral description of cloud processes allow more detailed forecasts. However, they are much more computationally expensive. Reducing the runtime of such simulations requires a highly parallel execution. This thesis presents a concept for coupling spectral cloud microphysics models with atmospheric models that allows for efficient utilization of today's available parallelism in the order of 100.000 processor cores. Due to the strong workload variations, highly scalable dynamic load balancing of the cloud microphysics model is essential in order to reach this goal. This is achieved through a hierarchical partitioning method based on space-filling curves. Furthermore, a highly scalable connection of dynamic load balancing and model coupling is facilitated by an efficient method to regularly determine the intersections between different partitionings. The results of this thesis enable the application of spectral cloud microphysics models for the simulation of realistic scenarios with high resolution grids by efficient use of high performance computers.