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There is evidence of an increased frequency of rapid intensification events of tropical cyclones (TCs) in global offshore regions. This will not only result in increased peak wind speeds but may lead to more intense heavy precipitation events, leading to flooding in coastal regions. Therefore, high impacts are expected for urban agglomerations in coastal regions such as the densely populated Pearl River Delta (PRD) in China. Regional climate models (RCMs) such as the Weather Research and Forecasting (WRF) model are state-of-the-art tools commonly applied to predict TCs. However, typhoon simulations are connected with high uncertainties due to the high number of parameterization schemes of relevant physical processes (including possible interactions between the parameterization schemes) such as cumulus (CU) and microphysics (MP), as well as other crucial model settings such as domain setup, initial times, and spectral nudging. Since previous studies mostly focus on either individual typhoon cases or individual parameterization schemes, in this study a more comprehensive analysis is provided by considering four different typhoons of different intensity categories with landfall near the PRD, i.e. Typhoon Neoguri (2008), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Usagi (2013), as well as two different schemes for CU and MP, respectively. Moreover, the impact of the model initialization and the driving data is studied by using three different initial times and two spectral nudging settings. Compared with the best-track reference data, the results show that the four typhoons show some consistency. For track bias, nudging only horizontal wind has a positive effect on reducing the track distance bias; for intensity, compared with a model explicitly resolving cumulus convection, i.e. without cumulus parameterization (CuOFF; nudging potential temperature and horizontal wind; late initial time), using the Kain–Fritsch scheme (KF; nudging only horizontal wind; early initial time) configuration shows relatively lower minimum sea level pressures and higher maximum wind speeds, which means stronger typhoon intensity. Intensity shows less sensitivity to two MP schemes compared with the CuOFF, nudging, and initial time settings. Furthermore, we found that compared with the CuOFF, using the KF scheme shows a relatively larger latent heat flux and higher equivalent potential temperature, providing more energy to typhoon development and inducing stronger TCs. This study could be used as a reference to configure WRF with the model's different combinations of schemes for historical and future TC simulations and also contributes to a better understanding of the performance of principal TC structures. [ABSTRACT FROM AUTHOR]

Realistic representation of rainfall characteristics on local scales by state-of-the-art climate models remains a key challenge, especially on sub-daily timescales. In this study, the convection-permitting Weather Research and Forecasting (WRF) model configured with 1.5 km grid spacing is used to simulate precipitation on sub-daily timescales over the Yangtze River Delta Region of China for continuous 10 years (2005–2014). The simulations are compared with rain gauge observations, reanalysis data, and the simulations of a lower resolution WRF with 9 km grid spacing that has a parameterization of convection. The results show that precipitation over the region can be well captured by using the convection-permitting model (CPM). Furthermore, the intensity, duration and coverage of these precipitation events can be more accurately described by the CPM. On the convection timescales of 1–4 h, especially for heavy rainfall events, the CPM is more accurate than the convection-parameterized model in capturing the short-duration events, which may be due to its better account of physical processes related to the convection on the convection-permitting scale. In addition, the extreme events which are more localized and with short-duration can be represented better by the CPM while the convection-parameterized model tends to produce widespread precipitation events covering more grid cells than observations Biases of the simulation by the 9-km mesh convection-parameterized mode appear to be related to the deficiencies in the representation of convections. [ABSTRACT FROM AUTHOR]

The arid and semi‐arid regions are highly vulnerable to climate change and variability. Agricultural production in these regions is particularly vulnerable because of its heavy dependence on climate conditions. Therefore, it is important to improve the projections of future agro‐climatic conditions. This study investigates the changes in agroclimatic conditions for growing Chinese Medicinal Yam (CMY) in the semi‐arid North China during 2031–2050 under the Representative Concentration Pathway (RCP) 4.5 and 8.5 emission scenarios. It employs high‐resolution climate data provided by the regional climate model (RCM) of Providing Regional Climates for Impacts Studies (PRECIS‐2.0). The changes in CMY yield and producing area under future climate change are simulated by the Agro‐Ecological Zone model. The simulations are based on newly calibrated cultivar parameters. The results show that the high‐resolution RCM simulation corresponds better with the observations of precipitation and temperature than the general circulation model (GCM). Because of the increased temperature and precipitation, the agroclimatic conditions for CMY are going to improve. While the traditional yam production area will maintain suitable for CMY production, the CMY production areas will further expand northward in the future. The average yield will increase by 1,196 and 1,336 kg DM/ha under RCP 4.5 and 8.5 emission scenarios, respectively. Key Points: Improving the projections of future agro‐climatic conditions in the semi‐arid North ChinaThe high‐resolution Providing Regional Climates for Impacts Studies projections correspond better with the observationsThe Chinese Medicinal Yam production areas will expand northward in the future, the average yield will increase by 1196–1336 kg DM/ha in the future [ABSTRACT FROM AUTHOR]

Since the 1980s, the industrialization and urbanization of the Beijing area has entered a period of high-speed growth. This paper asks the question: How have such great changes in urban land-use over the past decades impacted urban precipitation? In this study, we investigate and analyze the effects of urbanization on the summer precipitation in Beijing using numerical modeling approaches. Applying the numerical mesoscale atmospheric model METRAS, we determine the impact of surface cover on 13 heavy precipitation events. We implement five idealized land-use scenarios: Reference scenario, No-urban scenario, High-building scenario, Urban-expand scenario, and No-vegetation scenario. There is nearly no difference in the mean precipitation sum across all 13 simulated rain events and between the urban-scenarios and the rural-scenario. We find effects of urbanization on precipitation only in some single cases. We conclude urbanization does effect the local precipitation of Beijing; it reduces rainfall in the urban area and increases rainfall downwind of the city. In some cases, larger percentage of sealed area could give rise to the heavier precipitation or extreme rain events. And we conclude the urban pattern significantly impacts rainfall area and intensity. Increased urban size or density may speed up rain clouds while increased urban height may disrupt or bifurcate the clouds. Our results offer a new viewpoint and further the study of urban impacts on precipitation (UIP). The results are important for sustainable and harmonious development of the economy, society, and environment in Beijing as well as other cities with rapid urbanization. • The effects of urbanization on the precipitation can only be found in single cases. • Less rainfall in the urban area and more rainfall downwind of the urban area. • Extreme precipitation events could be more common in response to the urban expansion. [ABSTRACT FROM AUTHOR]