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This study investigates the role of convection representation across the gray zone in the multigrid nesting Weather Research and Forecasting (WRF) model based on two extreme precipitation events in Shanghai during the warm season. The precipitation forecast sensitivity and physical processes are examined using three convective treatments: (1) the traditional Kain-Fritsch (KF), (2) scale-aware Grell-Freitas (GF) cumulus parameterization, and (3) explicit convection (EC) solution and different nesting configurations consisting of grid spacings of 27, 15, 9, 5, 3, and 1 km using single, double, and triple nestings. At coarser grids (> 10 km), GF produces wet biases with delayed rainfall peaks, while KF produces dry biases with largely underestimated rainfall peaks. At a 9-km grid spacing, these biases were largely reduced, and using cumulus parameterizations (CUPs) shows specific advantages over the EC in the diurnal variation in extreme precipitation. GF performs better in simulating primary rainfall peak intensity, but KF performs better in simulating diurnal timing, albeit with underestimated intensity. The utilization of CUPs is beneficial at the 5-km grid in simulating both extreme intensity and diurnal variations, but it shows mixed effects at 3 km. When the 1-km grid with explicit convection was nested directly into their outermost 15-km or 9-km grids using KF CUP, avoiding treatment in the gray zone (5 km or 3 km), the primary rainfall peak at noon was best reproduced, but a secondary peak with a weak forcing was not detected. This peak was identified with local moisture divergence and a stable atmosphere. • Cumulus parameterization is beneficial with 9- and 5-km grids. • Kain-Fritsch can predict the diurnal rainfall peak better than the Grell-Freitas scheme for extreme rainfall in Shanghai. • Double nesting with a large ratio outperforms traditional triple nesting when forecasting the extreme rainfall in Shanghai. [ABSTRACT FROM AUTHOR]

The ability of climate models to capture extreme precipitation events is crucially important, but most of the existing models contain significant biases for the simulation of extreme precipitation. To understand the causes of these biases, we used five different cumulus parameterization schemes in the regional Climate–Weather Research and Forecasting (CWRF) model to investigate its performance and biases in the simulation of extreme precipitation events in China. In general, the ensemble cumulus parameterization (ECP) scheme was the most skillful in reproducing the spatial distribution of the 95th percentile daily precipitation (P95) and the other four schemes either overestimated (the Kain-Fritsch Eta and Tiedtke schemes) or underestimated (the Betts-Miller-Janjic and New Simplified Arakawa-Schubert schemes) P95. Compared with the observational data, ECP scheme significantly improved the simulation of extreme precipitation in China and had the highest correlation and the smallest root-mean-square error in most areas and seasons. To clarify the underlying physical processes of P95 simulation biases, we established a regression model of extreme precipitation based on ECP scheme. This showed that P95 in North China is mainly affected by moisture convergence, planetary boundary layer height and lifting condensation level (relative importance 18–32%). In Central China, the vertical upward motion of water vapor, sensible heat flux and planetary boundary layer height (relative importance 18–30%) are main factors associated with P95. In South China, the vertical upward motion and horizontal transport of water vapor are predominant (relative importance 26–37%). In addition, the net surface energy, surface and atmospheric radiation flux, total precipitable water, convective available potential energy and cloud water path also have a high correlation with P95 (the second most important factor; relative importance 14–31%). The influence of each factor on the simulation of P95 is different when using different cumulus parameterization schemes and the interaction among the different factors determines the ability of CWRF model to simulate extreme precipitation. These results provide important references for future model evaluations and improvements. • Five cumulus schemes were compared for their effects on China P95 using CWRF. • The ensemble cumulus parameterization outperformed the other schemes in simulating P95. • The underlying physical processes and mechanisms of P95 biases were explored. [ABSTRACT FROM AUTHOR]

The weak and sustained tropical cyclone (TC) Rumbia (2018) and its associated heavy precipitation after landfall are simulated by the Weather Research and Forecasting model using three microphysics parameterizations (MP) and two planetary boundary layer (PBL) schemes. Key factors regulating the heavy rainfall distribution of landed TC at three stages (landfall, inland slow-moving and recurving stage) and their sensitivities to multi-physics parameterizations are examined. Results show that heavy rainfall distribution is largely regulated by the intensity of TC itself at landfall, but more affected by environmental factors at the two inland stages, including the environmental vertical wind shear, the associated upper-level wind divergence and low-level moisture convergence. Different MP and PBL schemes mainly affect the simulation of TC thermodynamic structure and key environmental factors, leading to their different forecast skills at three stages. Specifically, the Yonsei University (YSU) PBL scheme systematically outperforms the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme in simulating stronger TC and heavy rainfall intensity. With the advantageous YSU PBL scheme, the Ferrier MP scheme only shows a slight advantage in simulating the intensity of TC and heavy rainfall at landfall. However, the WRF single-moment 6-class (WSM6) and Thompson scheme produce more graupel or snow, and better simulate the key environmental factors, showing their respective advantages in simulating the heavy rainfall structure and location at the two inland stages. This implies that the use of nonlocal YSU PBL scheme and the MP schemes with sophisticated ice processes shows superiority in simulating the postlandfall heavy rainfall induced by weak TCs. • Postlandfall rainfall of weak TC is highly affected by environmental factors. • Ice species and radial distribution of diabatic heating affect TC structures and rainfall. • The MYNN PBL scheme underestimates TC intensity and heavy rainfall compared to YSU. [ABSTRACT FROM AUTHOR]