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The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980-2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities. [ABSTRACT FROM AUTHOR]

Climate changes over China from the present (1990-1999) to future (2046-2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility. [ABSTRACT FROM AUTHOR]

A regional Climate‐Weather Research and Forecasting (CWRF) model with a 30‐km horizontal resolution was applied to simulate the East Asian summer monsoon (EASM) and climate in China from 1980 to 2016. As compared with observations and reanalysis data, the model can reasonably reproduce the spatial distributions of the climatological mean atmospheric circulation and water vapour transport in East Asia, as well as the seasonal advance and retreat of EASM and rain bands. The correlation coefficient between the EASM circulation index in the simulation and reanalysis is.97. The model can well represent the geographic distributions of summer mean temperature and precipitation over most of China. However, model biases still exist, in particular the skill in the Yangtze–Huaihe River basin is relatively low. The simulated climatological mean temperature and precipitation deviations from observations may be related to the model's systematic circulation biases, with a thicker lower troposphere (i.e., higher temperature between 500 and 1,000 hPa) over most of China (except for the south and southwest) and a thinner one over the coastal oceans in summertime. Compared with the reanalysis, the model overestimates the land‐ocean thermal contrast, which causes the South Asian High shifted to the north and east, as well as the subtropical high and tropical convective activity located further north and persisted longer. Consequently, more water vapour transports northward, leading to more precipitation over North China–Northeast China and less precipitation over the Yangtze and Huaihe River basin. Therefore, the enhanced land‐ocean thermal contrast may be a major factor for the stronger EASM in the model, and corresponding precipitation and temperature biases in China. This analysis provides important information for further improving model performance in EASM simulation. [ABSTRACT FROM AUTHOR]