Simulation experiment on "23·7" extreme precipitation in North China using the GRIST model

GRIST (Global-Regional Integrated forecast SysTem), the kilometer-scale variable-resolution (VR) model, combines the virtues of both global and regional models. It can provide medium-term forecast products without lateral conditions while it is expected to reach a forecast level similar to that of high-precision regional models. To evaluate GRIST' s ability to simulate extreme weather events, this study uses the VR version of GRIST to conduct a hindcast experiment on the extremely heavy rainfall event that occurred in North China from July 29 to August 2, 2023. The simulation results are evaluated against satellite-gauge merged precipitation analysis (China Merged Precipitation Analysis, CMPA) and global reanalysis (the fifth generation of the European Center for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate, ERA5) data, and are also compared with five other operational numerical models (including global models, ECMWF and CMA-GFS, as well as regional models, CMA-MESO, CMA-SH, and CMA-BJ). Results indicate that both GRIST and five operational models can reproduce the occurrence of this heavy rainfall event at various degrees. From 08 BT 30 July to 08 BT 1 August, the period with the strongest precipitation during the event, the simulation performance of GRIST is optimal. The spatial correlation coefficient between GRIST and CMPA can reach up to 0.85, and the TS scores of precipitation at various intensities are at the levels of regional model forecasts. GRIST accurately simulated the large-scale circulation characteristics during this process, encompassing the location and extent of the Western Pacific Subtropical High, northward water vapor transport. Furthermore, it demonstrated a commendable capability in simulating the distribution and intensity of storm-relative helicity, a key indicator of the local circulation. The precise representation of circulation conditions at various scales by GRIST contributes significantly to its ability to characterize precipitation areas and the evolution of precipitation.