Based on the high-resolution grid observation dataset CN05.1 and the regional climate model RegCM4 dynamical downscaling simulations (i.e., CdR, EdR, HdR, and MdR), the cluster high temperature events (CHTE) occurring in China during 1981-2005 were identified using a simplified objective method. Then, the performance of the RegCM4 dynamical downscaling simulations on the CHTE in China was evaluated through the comparison with the observation. The results indicate that the four RegCM4 simulations and their ensemble mean (MME) can reproduce the observed climatological distribution of the frequency, duration and cumulative intensity of the CHTE in China. However, the frequency of the CHTE in Xinjiang is slightly underestimated by the HdR simulation while that in southeastern China is slightly overestimated by the CdR, EdR and MdR simulations. The characteristics of the probability in duration, extreme intensity, cumulative intensity, maximum influential area, average influential area, and comprehensive intensity for the CHTE can also be reasonably captured by the MME and its ensemble members. In addition, the MME simulates an upward trend, which is consistent with the observation, in the comprehensive intensity index and the individual indices of the CHTE in China. The individual simulations can also reproduce the upward trend for most of the indices. However, some deficits are also found. For instance, the EdR simulation produces a downward trend for the comprehensive intensity of the CHTE, and the MdR simulation shows a weak downward trend for the frequency and extreme intensity of the CHTE.

Climate warming will lead to accelerate the melting speed in high-mountain cryosphere. On the one hand, it develops the uncertainty of the spatiotemporal distribution, on the other hand, it causes changes in the frequency and intensity of snowmelt floods. Based on meteorological, hydrological and MODIS snow cover data, using the Snowmelt Runoff Model (SRM) to simulate and verify the spring runoff result during the snowmelt period from 1990 to 2012 in Zarmsk controlling area in the Heihe River basin. Results show SRM has a high accuracy (Nash-Sutcliffe efficiency coefficient = 0.91), which can be used to predict the future flood intensity changes in studying area. In order to predict the trends under the different future climate change background, the temperature and precipitation downscaling data were used. The results show that the maximum snow cover area can be reduced about 3%-7% in different RCPs scenario compared with the reference period, and the change is more relative to the increase of altitude. By the end of this century, the flood intensity shows different changes according to climate change situations compared with the reference period: in RCP2.6, due to the small changes of the temperature and precipitation, it will change slightly within 10%; in RCP4.5, it will increase about 20%; in RCP8.5, may be rise around 30%. The result of correlation analysis shows that the correlation between flood intensity and temperature/precipitation is strong for different return period floods: the longer return period, the higher correlation between flood peak flow and temperature; and the shorter return period, the higher correlation between flood peak flow and precipitation. By projecting the frequency and intensity of snowmelt flood events under climate change, it is helpful to analyze the potential risk scope, carry out regional flood risk management and increase the value of flood water resources.

The Ganjiang River basin (GJRB) and the Guanting River basin (GTRB) were selected to study the impacts of climate change on river runoff in the eastern monsoon region. HBV hydrological model was calibrated and validated based on observed daily meteorological and discharge data. Five sets of downscaled and bias corrected outputs of GCM from CMIP5 were used to drive HBV model to assess the climate change impacts in the 21st century under three Representative Concentration Pathways (RCP2.6, RCP4.5, RCP8.5). The results indicate that (1) annual mean temperature from 1961 to 2017 showed a significant upward trend with rate of 0.17℃/(10 a) and 0.28℃/(10 a), respectively, in the GJRB and the GTRB. Increase of air temperature in the GTRB was faster than the GJRB. Meanwhile, annual precipitation increased significantly in the GJRB, but a weak decrease was detected in the GTRB. Under 3 RCP, both GJRB and GTRB were projected to be in a warmer and wetter climatic condition in the 21st century, but increase of temperature and precipitation in the GTRB will be more notable than the GJRB. (2) In the 21st century, the increase of annual and seasonal runoff in the GTRB will be greater than that in the GJRB. The annual runoff in the GTRB will show a consistent increase trend in the near term (2020-2039), mid-century (2050-2069) and late century (2080-2099), with the largest increase under RCP8.5 and the smallest under RCP4.5. As for the GJRB, annual runoff for the near term and the mid-century may decrease slightly relative to the baseline (1986-2005), but the runoff trend in the entire 21st century will be positive under RCP4.5. Under RCP2.6 and RCP8.5, increase of runoff will be weakened after the mid-21st century. (3) Flood risk in the GTRB and drought risk in the GJRB will be aggregated in the 21st century.

Global warming impacts spatial and temporal distribution of hydrology and water resources. Understanding the spatial and temporal distribution characteristics of future water resources variations is of great significance for the protection and development of the Yangtze River basin. In this study, bias corrected outputs of a multi-model ensemble were used to drive a two-parameter water balance model to investigate the runoff responses to 1.5℃ and 2.0℃ global warming targets as introduced by The Paris Agreement for the Yangtze River basin. The results showed that: (1) The bias corrected outputs of the multi-model ensemble have good agreement with observed annual mean precipitation and potential evapotranspiration patterns of the Yangtze River basin. (2) The two-parameter water balance model combining with parameter regionalization show reasonable performance in simulating monthly runoff in the Yangtze River basin. (3) Both the annual and seasonal runoffs are projected to increase under the 1.5℃ global warming. Compared to the reference period (1976-2005), the runoff is projected to increase by 5% for more than 50% of the third-level sub-basins under 1.5℃ global warming; that is projected to increase by 8% under the 2.0℃ global warming. This indicates that water resources in the Yangtze River basin will increase further under the 2.0℃ global warming. (4) In terms of spatial variability, the precipitation in the northern part of the Yangtze River basin is projected to increase more than other regions for both two warming scenarios. The distribution pattern of runoff increase is almost the same as the precipitation. The increasing amplitude of the runoff over the Hanjiang River basin is projected to be the highest across the whole basin.

Based on the ozone remote sensing data of SBUV(/2), combined with ERA-Interim and MERRA-2 reanalysis data, the trends of ozone and atmospheric temperature in Lhasa and Gonghe were investigated in spring during 1979-2018. The results show that the reversal trend of ozone and atmospheric temperature in Lhasa and Gonghe both occurred in 1999. Compared with the overall ozone changing rate of the Tibetan Plateau after 2008 (4.5 DU/(10 a)), the rate of Lhasa is faster, which is 5.9 DU/(10 a), and the rate of Gonghe is relatively slow, which is 3.7 DU/(10 a). Since 1999, the atmospheric temperature in the lower stratosphere (100-30 hPa) in Lhasa and Gonghe in spring have increased with the rates of 0.5-1.4℃/(10 a) and 0.01-0.9℃/(10 a), respectively, while the atmospheric temperature in the upper troposphere (250-175 hPa) have decreased with the rates of 0.2-1.5℃/(10 a) and 0.2-1.2℃/(10 a), respectively. Compared with the overall atmospheric temperature change of the plateau in 2008, the temperature increase rate is slower than that of the lower stratosphere (125-70 hPa, 1-2℃/(10 a)), and faster than that of the upper troposphere (225-175 hPa, 0.4-1.1℃/(10 a)). In view of the correlation coefficient and regression coefficient between the temperature and ozone, the different recovery rates of ozone in Lhasa and Gonghe in spring after 1999 may lead to the different rates of stratospheric-tropospheric temperature reversal in the same period.

Based on the re-forecast and operational data from the second-generation seasonal prediction model of Beijing Climate Center (BCC_CSM1.1m), and the monthly observational precipitation of 66 stations over Fujian province in 1991-2017, the precipitation prediction ability of the Model during the pre-flood season at different lead time was assessed. The metrics of verification used in this study were anomaly correlation coefficient (ACC), temporal correlation coefficient (TCC), mean square skill score (MSSS) and the prediction score (Ps). The system bias correction (BC), the linear regression (LR) and Empirical Orthogonal Function-analogue correction were used to revise the forecast. Results show that: (1) Although there is always a systematic negative bias in the prediction of the climatological precipitation for the pre-flood season over Fujian province at different leading time, the Model can predict the first and second typical modes of the precipitation in the pre-flood period: the uniform distribution in the whole province and the decrease from south to north; (2) The inter-annual variation of ACC skill and Ps scores of the precipitation prediction are prominent, and the MSSS scores are negative due to the systematic negative bias. The high TCC skill can be found in the northern Fujian province; (3) The prediction ability of the model for precipitation improved significantly after being corrected. The average Ps scores in 2011-2017 are 5.9, 3.5, 6.7 and 7.8 points higher than the raw ensemble at LM2 (leading two months) after the BC, LR, EOFL and EOFNL correction, respectively. The average ACC skill scores in 2011-2017 are 0.02, 0.21, 0.12 and 0.11 points higher than raw predictions after the LR correction. There are significant improvements of MSSS scores for the four correction methods, of which the bias correction and the linear regression correction receive positive techniques; (4) In general, the linear regression correction shows more advantages than the other three correction methods.

Based on the time series of the gross domestic product and the demographic census of China, and statistical yearbooks in 31 provinces (China mainland), the labor participation rate, capital output elasticity and total factor productivity were localized in order to build the Cobb-Douglas model under the Shared Socioeconomic Pathways (SSPs) framework. The changes of national and provincial output value of the primary, secondary and tertiary industries were analyzed for the first half of the 21st century. The results show that: (1) The output value of three industries in China will be different significantly under SSPs. The primary and secondary industries show the same trend, but output value of the latter will be higher than that of the former. And they will increase under SSP1, SSP2 and SSP5, while decline under SSP3 and SSP4. The tertiary industry will continuously grow under all SSPs. (2) The growth rate of output value of three industries will decrease. The differences of industrial growth rates and material and cultural need lead to the gradual decrease of contribution of the primary and the secondary industries to economy, while the increase of contribution of the tertiary industry. (3) Different social and economic development policies have significant impacts on the provincial output value of three industries. Spatially, the output value of the primary, secondary and tertiary industries is high in the east but low in the west in China in 2020-2050. By the mid-21st century, increase in the output value in the eastern region will be the highest for three industries. The increase of the output value of the primary and secondary industries in the northeast region will be the lowest, while that of the tertiary industry in the western region will be the lowest. In 2050, contribution of the primary industry to provincial GDP will be less than 8% and that of the tertiary industry will be higher than 60% generally. Meanwhile, share of the secondary industry will be 25%-30%. The contribution of the output value of the tertiary industry to GDP in the eastern region will be always higher than that in the west, and industrial structure of the eastern region is more reasonable.

Under the overall environment that the whole country is advocating energy conservation and emission reduction and seeking low-carbon economic development, exploring the correlation between various impact factors and carbon emissions at different income levels is conducive to the formulation of regional heterogeneous carbon emission reduction policies. Based on the data from 2002 to 2016, China's 30 provinces (except Tibet, HongKong, Macao and Taiwan) were divided into four income levels, and a panel vector autoregressive (PVAR) model was established. Moreover, Granger causality test, impulse response and variance decomposition were used to explore the correlation between urbanization, industrial structure, energy consumption, economic growth and carbon emissions. The results show that there is heterogeneity in the relationship between the impact factors and carbon emissions in the provinces at different income levels. The urbanization of provinces at higher income level has produced significant emission reduction effect, while the less developed areas are still in the stage which urbanization promotes carbon emissions. Energy consumption at all four income levels will affect carbon emissions in the long run. However, less developed areas have a more urgent need for getting rid of energy dependence for economic development, and should improve energy efficiency to reduce carbon emissions. The impact of industrial structure on carbon emissions in non-high-income provinces is significantly higher than that in high-income provinces. In addition, the empirical results show that China is still in the stage of synchronous growth of carbon emission and economic development, and emission reduction policies will generate negative feedback on economic growth. Therefore, the selection of existing emission reduction path needs highly prudent design and implementation.

The national carbon market will start in power sector in China, and its core component is allowance allocation. Thus comparative analysis of Emissions Trading Scheme (ETS) pilots allocation will provide experiences and lessons for national scheme. Our study shows that 202 electric power enterprises are covered in ETS pilots, and there are similarities and differences in their coverage, information disclosure, allocation methodology and offset mechanism, with common problems such as weak basis for baseline methodology, improper treatment of indirect emissions, and difficulty in allocating power and heat emissions for combined heat and power facilities. It is suggested that the general principles for allowance allocation of national carbon market are to proceed through phases with coordinated efforts in order to achieve stability, flexibility, and transparency in a scientific and practical manner.