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This paper preliminarily evaluates the speedup, scalability, and prediction skill of the high-performance advanced regional eta coordinate model (H-AREM), which is based on several parallel processing methods and decomposition strategies. Results show that the parallel version of the model that is based on a modular parallel framework and a multidimensional domain decomposition strategy performs better overall, e.g. it is faster and more scalable than the version based on a message passing interface and a one-dimensional decomposition strategy. In particular, the scalability of the H-AREM with a resolution of 8 km approaches 8099 cores. Moreover, in the H-AREM, higher resolutions result in more realistic precipitation predictions without remarkable increases in simulation time.