Mesoscale convective systems (MCSs) are active in East China during the summer, causing significant precipitation and extreme weather. Increasing MCS frequency and intensity with climate change highlights the need for better simulation and forecasting. Traditional global and regional models with coarse resolution unable to explicitly resolve convection fail to represent MCSs and their precipitation accurately. This study conducted a 22-year (2000–2021) JJA simulation at a convection-permitting resolution (4 km grid spacing) using the WRF model (WRF-CPM) over East China. The WRF-CPM model’s ability to reproduce MCSs was evaluated against satellite infrared-retrieved cloud top temperature, IMERG V06 precipitation, and global reanalysis data ERA5. Results show that WRF-CPM captures the observed MCS frequency and precipitation patterns but overestimates them in most areas. The model also accurately simulates the eastward propagation of MCSs, albeit at a slightly faster speed and longer duration. MCSs in WRF-CPM exhibit realistic life cycles in terms of cloud top temperature, convective core area, and precipitation. WRF-CPM tends to overestimate rainfall frequency over 20 mm/h while underestimates rainfall per MCS, possibly due to an overestimated number and area. The model captures the diurnal cycle of MCSs well in most of East China, though it shows a 2-hour delay in southeast China and fails to reproduce the midnight peak to the east of Tibetan Plateau, probably because of model’s limited ability to represent thermal diurnal variation over complex topography. WRF-CPM captures the shear effect on MCS precipitation, indicating increased precipitation with stronger shear and higher total column water vapor.

Tropical cyclones (TCs) simulated at gray-zone resolution (9km) in the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) are performed and evaluated over the Western North Pacific (WNP). Two sets of experiments are carried out to test the effect the cumulus parameterization (CPS), driven by the European Center for Medium-Range Weather Forecasts (ECMWF) fifth-generation global atmospheric reanalysis (ERA5), over 11-year (2008-2018) TC seasons (June–November). Through comparisons with the observation and reanalysis, it is shown that the ICPS (involving CPS) experiment yields good skills in simulating TC frequency due to more realistic large-scale mean states, including stronger low-tropospheric circulation, wetter mid-tropospheric environment and more active ascending motion over TC main development region (MDR). On the other hand, the NICPS (not involving CPS) experiment is found to better reproduce intense TCs (Saffir-Simpson hurricane categories 3, 4 and 5) in terms of intensity, accumulated cyclone energy (ACE) and inner-core size. Although NICPS can capture TC inner-core structure well with stronger radial inflow in the boundary layer, rising motion around the eyewall and outflow in the mid to upper troposphere, NICPS significantly underestimates TC outer size due to rapidly decay of TC outer wind field, suggesting limited severe convective activities in the outer region.

High-resolution regional reanalysis is one of the most powerful tool to study local and regional high-impact weather events, climate extremes and climate change impact. In this study, two high-resolution Chinese Regional Reanalysis (CNRR) datasets with a resolution of 18km over 1998-2012, which are produced using the Gridpoint Statistical Interpolation (GSI) data assimilation system and spectral nudging (SN) technique, were assessed. The reliability of the surface and upper air variables of the CNRR datasets was evaluated by comparing with in-situ observations and the European Centre for Medium-Range Forecasts (ECMWF) ERA-Interim (ERAIN) global reanalysis dataset. The results show that the CNRR can provide more accurate near-surface variables than the driving ERAIN global reanalysis. However, special care should be taken when using the CNRR precipitation dataset, especially for heavy rainfall cases. CNRR datasets are also able to generate high-quality upper atmospheric products especially in CNRR-GSI experiment, which assimilates long time series of local observations. By using the three-dimensional variational data assimilation (3D-Var) method, CNRR-GSI outperforms the CNRR-SN and ERAIN. With the increase of the computing resources, the potential opportunities for improving CNRR can be expected by applying more advanced methods.

In this study, two Weather Research and Forecasting model (WRF) experiments with gray-zone (GZ9) and convection-permitting (CP3) resolution are conducted for summer from 2009 to 2019. The surface air temperature (T2m) and precipitation are evaluated against in-situ observations and the Global Satellite Mapping of Precipitation (GSMaP) dataset. The results show that both experiments successfully capture the spatial pattern and daily variation of T2m and precipitation, though cold bias for temperature and dry bias for precipitation exist especially over the regions south of 35°N. Compared to GZ9, CP3 reduces the cold and dry bias over the southern TP. In addition, analysis of the diurnal variation of precipitation shows that both experiments simulate the advanced occurrence time of maximum precipitation over the eastern TP but postpone that over the central and western TP. Both experiments simulate a bimodal structure of the diurnal cycle of precipitation amount (PA). Further investigation reveals that GZ9 has more low-level clouds and prevents shortwave radiation from reaching the surface during daytime, leading to lower maximum surface air temperature (Tmax) in GZ9, while CP3 has more low-level clouds over the southeastern TP and preventing the outgoing longwave radiation and compensating the heat loss during nighttime, resulting in higher minimum surface air temperature (Tmin) in CP3. Besides, more water vapor over the southeastern TP results in more precipitation and thus reduced dry bias in CP3 over the southeastern TP.

Based on the object-based tracking algorithm, the precipitation simulation ability of the convection-permitting (CP) regional climate models (RCMs) is evaluated from the viewpoints of the precipitation systems in this work. The characteristics of precipitation systems over eastern China during 1998-2007 derived from the Weather Research and Forecasting model (WRF) with the horizontal grid spacing of ~ 4 km are compared with CMORPH. On the whole, CP RCMs can capture the average duration and eccentricity of all precipitation systems reasonably. However, precipitation systems tend to be stronger but with smaller coverage area in CP RCMs, which leads to the wet biases and dry biases of accumulated precipitation amount in the longer-duration (>= 48 hr) and shorter-duration (< 48 hr) systems, respectively. Such deficiencies in accumulated precipitation amount of precipitation systems with various durations can be made up by employing spectral nudging technique in CP RCMs to a certain degree. This work further indicates that, to improve the capability of precipitation simulation in CP RCMs, the relationship between intensity and coverage area of precipitation systems should be described properly in CP RCMs, especially for those with shorter-duration.