Abstract. Biomass burning is a main source for primary carbonaceous particles in the atmosphere and acts as a crucial factor that alters Earth's energy budget and balance. It is also an important factor influencing air quality, regional climate and sustainability in the domain of Pan-Eurasian Experiment (PEEX). During the exceptionally intense agricultural fire season in mid-June 2012, accompanied by rapidly deteriorating air quality, a series of meteorological anomalies was observed, including a large decline in near-surface air temperature, spatial shifts and changes in precipitation in Jiangsu province of East China. To explore the underlying processes that link air pollution to weather modification, we conducted a numerical study with parallel simulations using the fully coupled meteorology–chemistry model WRF-Chem with a high-resolution emission inventory for agricultural fires. Evaluation of the modeling results with available ground-based measurements and satellite retrievals showed that this model was able to reproduce the magnitude and spatial variations of fire-induced air pollution. During the biomass-burning event in mid-June 2012, intensive emission of absorbing aerosols trapped a considerable part of solar radiation in the atmosphere and reduced incident radiation reaching the surface on a regional scale, followed by lowered surface sensible and latent heat fluxes. The perturbed energy balance and re-allocation gave rise to substantial adjustments in vertical temperature stratification, namely surface cooling and upper-air heating. Furthermore, an intimate link between temperature profile and small-scale processes like turbulent mixing and entrainment led to distinct changes in precipitation. On the one hand, by stabilizing the atmosphere below and reducing the surface flux, black carbon-laden plumes tended to dissipate daytime cloud and suppress the convective precipitation over Nanjing. On the other hand, heating aloft increased upper-level convective activity and then favored convergence carrying in moist air, thereby enhancing the nocturnal precipitation in the downwind areas of the biomass-burning plumes.

Statistical downscaling and dynamical downscaling are two approaches to generate high‐resolution regional climate models based on the large‐scale information from either reanalysis data or global climate models. In this study, these two downscaling methods are used to simulate the surface climate of China and compared. The Statistical Downscaling Model (SDSM) is cross validated and used to downscale the regional climate of China. Then, the downscaled historical climate of 1981–2000 and future climate of 2041–2060 are compared with that from the Weather Research and Forecasting (WRF) model driven by the European Center‐Hamburg atmosphere model and the Max Planck Institute Ocean Model (ECHAM5/MPI‐OM) and the L'Institut Pierre‐Simon Laplace Coupled Model, version 5, coupled with the Nucleus for European Modelling of the ocean, low resolution (IPSL‐CM5A‐LR). The SDSM can reproduce the surface temperature characteristics of the present climate in China, whereas the WRF tends to underestimate the surface temperature over most of China. Both the SDSM and WRF require further work to improve their ability to downscale precipitation. Both statistical and dynamical downscaling methods produce future surface temperatures for 2041–2060 that are markedly different from the historical climatology. However, the changes in projected precipitation differ between the two downscaling methods. Indeed, large uncertainties remain in terms of the direction and magnitude of future precipitation changes over China.

The changes in mean and extreme climate in China during 2020–2060 are detected with both Weather Research and Forecasting and RegCM4, by downscaling the simulations from EC‐EATTH and IPSL‐CM5A under both the RCP4.5 and RCP8.5 scenarios. The climate changes under the two scenarios exhibit similar patterns, with stronger intensity under the RCP8.5 scenario. For the mean precipitation, increases are projected in most regions, with the largest relative increase in the Tarim Basin. Slight drought mainly occurs in the south‐eastern part of China. The frequency of drizzle rain is expected to decrease in all the sub‐regions, but the moderate to heavy rainfall as well as the storm would occur more frequently, especially on the Tibetan Plateau. The whole country would experience much warmer climate in the future, with the strongest warming over the Tibetan Plateau. By detecting the changes in climate extremes, it is indicated that less dry extremes would occur in the wet areas of China, while more dry events in the arid and semiarid regions. The wet extreme indices would increase in most regions, especially in the wet areas. The surface air temperature tends to become extremely warmer in the future over the whole country, with the strongest change over the Tibetan Plateau. The changes in mean and extreme climate depend strongly on the driving global climate models, with wetter and warmer climate in the downscalings over IPSL‐CM5A, and the model physics of the regional climate models also exert great impact on the projections. Finally, the possible mechanisms for the changes of extreme precipitation are discussed. The enhanced summer monsoon in the future transports more moisture to China, which could lead to more summer precipitation. As a result, the wet extremes tend to increase.

Under the framework of the Regional Climate Model Intercomparison Project (RMIP III), simulation results from six regional climate models (RCMs) and two global climate models (GCMs) were used to generate climate extreme indices for the present and future over China using two ensemble methods. All the models reasonably captured the observed climate extremes, and performance‐based ensemble averaging (PEA) outperformed the individual model and equal‐weighted averaging (MME) for the control climate. However, noticeable cold deficiencies in temperature extremes were found over areas with complex topography, and too frequent heavy precipitation at smaller intensities was simulated using the multiple model ensembles. Under the A1B scenario for 2041–2060, widespread increases in the 90th percentiles of the maximum temperatures (Tmax90p) and the 10th percentile of the minimum temperatures (Tmin10p) were projected, with larger increases in winter than in summer. Greater intensities in precipitation extremes were projected over China, with the exception of Inner Mongolia. Large uncertainties exist in the projected mean diurnal temperature range (Trange), number of days with precipitation exceeding 10 mm (R10) and the maximum number of consecutive dry days (CDD) because of disagreements in both the magnitudes and signs of the climate model projections, and even the two ensemble methods presented opposite signs over some regions.