Stratospheric aerosol geoengineering has been proposed as a potential solution to reduce climate change and its impacts. Here, we explore the responses of the Hadley circulation (HC) intensity and the intertropical convergence zone (ITCZ) using the strategic stratospheric aerosol geoengineering, in which sulfur dioxide was injected into the stratosphere at four different locations to maintain the global-mean surface temperature and the interhemispheric and equator-to-pole temperature gradients at present-day values (baseline). Simulations show that, relative to the baseline, strategic stratospheric aerosol geoengineering generally maintains northern winter December–January–February (DJF) HC intensity under RCP8.5, while it overcompensates for the greenhouse gas (GHG)-forced southern winter June–July–August (JJA) HC intensity increase, producing a 3.5 ± 0.4% weakening. The residual change of southern HC intensity in JJA is mainly associated with stratospheric heating and tropospheric temperature response due to enhanced stratospheric aerosol concentrations. Geoengineering overcompensates for the GHG-driven northward ITCZ shifts, producing 0.7° ± 0.1° and 0.2° ± 0.1° latitude southward migrations in JJA and DJF, respectively relative to the baseline. These migrations are affected by tropical interhemispheric temperature differences both at the surface and in the free troposphere. Further strategies for reducing the residual change of HC intensity and ITCZ shifts under stratospheric aerosol geoengineering could involve minimizing stratospheric heating and restoring and preserving the present-day tropical tropospheric interhemispheric temperature differences.

Based on the historical and future outputs of 17 coupled model intercomparison project phase 5 (CMIP5) models, simulation of the precipitation extremes in China was evaluated under baseline climate condition compared to a gridded daily observation dataset CN05.1. The variations in precipitation extremes for eight global warming targets were also projected. The 17 individual models and the multi-model ensemble accurately reproduced the spatial distribution of precipitation extremes, although they were limited in their ability to capture the temporal characteristics. A notable dry bias existed in Southeast China, while a wet bias was present in North and Northwest China. The precipitation extremes in China were projected to be more frequent and more intense as global temperature rise reached the 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 °C warming targets. The projected percentage changes in the annual number of days with precipitation >50 mm (R50) and total precipitation during days in which the daily precipitation exceeds the 99th percentile (R99p) are projected to increase by 25.81 and 69.14 % relative to the baseline climate for a 1.5 °C warming target, and by 95.52 and 162.00 % for a 4.0 °C warming target, respectively. As the global mean temperature rise increased from 1.5 to 5 °C, the subregions considerably affected by the East Asian summer monsoon (e.g., Southwest China, South China, and the Yangtze-Huai River Valley) were projected to experience a more dramatic increase in extreme precipitation events, in both number of days and intensity, while North and Northwest China were projected to suffer from relatively slight increases. The model uncertainties in the projected precipitation extremes in China by 17 CMIP5 models increase as global temperature rise increases.

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.