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The Three Gorges Project, the largest hydroelectric project in the world, has attracted widespread attention regarding its impact on regional climate. However, existing studies on the climate effects of the Three Gorges Project construction are not sufficient due to limited data accumulation. In this study, we analyzed the annual and seasonal trend changes in temperature, precipitation, and humidity over the Three Gorges Reservoir Area (TGRA) based on long-term meteorological stations data, remote sensing data, and reanalysis products. Observation minus reanalysis method (OMR) was used to reveal possible impacts of land cover changes on climate changes. Major results indicated that the TGRA experienced an overall warming trend for both annual and seasonal variations, with greater rising trends in the upstream. Except for autumn, the relative humidity of most regions mainly showed significant downward trends, indicating an overall drying trend in the TGRA. There was insignificant change in total precipitation and precipitable water vapor, with the largest variation observed during the summer. Although there were small differences among these datasets, their results of climate changes showed good consistency overall. In addition, the results of OMR indicated that land cover changes mainly had a warming and drying effect on the middle and upper reaches, and a cooling and moistening effect on the lower reaches of the TGRA. This may be due to the impact of land cover changes on the surface energy balance, thus affected temperature and humidity. The study has important reference value for understanding the climate changes in the TGRA and the climate effects brought about by large-scale engineering construction.

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Based on the downscaling data of multi-model ensembles of 26 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6, this study calculated the extreme climate indices defined by the Expert Team on Climate Change Detection and Indices and the warm winter extreme grade indices to explore winter climate response on the Chinese mainland under different shared socioeconomic pathways (SSPs) and representative concentration pathways. The results showed that the temperature in winter increased overall, with the highest temperature increases of 0.31 °C/10a (Celsius per decade) (SSP245) and 0.51 °C/10a (SSP585) and the lowest temperature increases of 0.30 °C/10a (SSP245) and 0.49 °C/10a (SSP585). Warm-related extreme weather events such as warm days and warm spell duration indices showed an increasing trend, whereas cold-related extreme weather events such as cold spell duration indices, cold nights, ice days, and frost days showed a decreasing trend. On the regional scale, the maximum temperature increased by more than 2 °C/10a (SSP245) and 0.4 °C/10a (SSP585), except in South China, and the minimum temperature increased faster in Qinghai-Tibet and Northeast China compared to elsewhere on the Chinese mainland. Compared with that under SSP585, the frequency and intensity of warm winters in the latter half of the 21st century were lower under SSP245. At the end of the 21st century, under the SSP245 scenario, warm winter frequency in most regions will be reduced to below 60%, but under the SSP585 scenario, it will be more than 80%. Population exposures all showed a downward trend, mainly due to the reduction of warm winter events and the decline of the population under the SSP245 and SSP585 scenarios, respectively. If the greenhouse gas emission path is controlled in the SSP245 scenario, the population exposure risk in warm winters can be decreased by 25.87%. This study observed a consistent warming trend on the Chinese mainland under all SSPs in the 21st century and found that stricter emission reduction policies can effectively decrease the population exposure to warm winters.

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The Yellow River Basin (YLRB) and Yangtze River Basin (YZRB) are heavily populated, important grain-producing areas in China, and they are sensitive to climate change. In order to study the temporal and spatial distribution of extreme climate events in the two river basins, seven extreme temperature indices and seven extreme precipitation indices were projected for the periods of 2010-2039, 2040-2069, and 2070-2099 using data from 16 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and the delta change and reliability ensemble averaging (REA) methods were applied to obtain more robust ensemble values. First, the present evaluation indicated that the simulations satisfactorily reproduced the spatial distribution of temperature extremes, and the spatial distribution of precipitation extremes was generally suitably captured. Next, the REA values were adopted to conduct projections under different representative concentration pathway (RCP) scenarios (i.e., RCP4.5, and RCP8.5) in the 21st century. Warming extremes were projected to increase while cold events were projected to decrease, particularly on the eastern Tibetan Plateau, the Loess Plateau, and the lower reaches of the YZRB. In addition, the number of wet days (CWD) was projected to decrease in most regions of the two basins, but the highest five-day precipitation (Rx5day) and precipitation intensity (SDII) index values were projected to increase in the YZRB. The number of consecutive dry days (CDD) was projected to decrease in the northern and western regions of the two basins. Specifically, the warming trends in the two basins were correlated with altitude and atmospheric circulation patterns, and the wetting trends were related to the atmospheric water vapor content increases in summer and the strength of external radiative forcing. Notably, the magnitude of the changes in the extreme climate events was projected to increase with increasing warming targets, especially under the RCP8.5 scenario.

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Pan evaporation (EVP) is an important element of the hydrological cycle and exhibits a close relationship with climate change. In this study, the generalized regression neural network (GRNN) model and extreme gradient boosting (Xgboost) model were applied to estimate the monthly EVP. The spatiotemporal distributions of EVP and influencing factors in China and eight subregions from 1961 to 2017 were analyzed. The root mean square error (RMSE) of all GRNN models was approximately 10%, and the Nash-Sutcliffe efficiency (NSE) coefficient was larger than 0.94 in different subregions. The annual mean EVP in all subregions and throughout China showed decreasing trends before 1993, while EVP increasing trends occurred in East China (EC), South China (SC), Southwest China (SWC), west of Northwest China (WNC), and throughout China after 1994. Subsequently, the variable importance in projection (VIP) between EVP and climatic factors obtained by partial least squares (PLS) regression and the relative contribution calculated by Xgboost stepwise regression analysis (SRA) were used to investigate the climatic parameter sensitivity to EVP. The results indicated that the combined effects of the vapor pressure deficit (VPD), sunshine duration (SSD), and wind speed (WIN) were the main reasons for the variations in EVP across China. At the seasonal scale, SSD, WIN, relative humidity (RHU), and VPD were the most sensitive climatic factors to EVP in different seasons. In addition, the Pacific decadal oscillation (PDO) index showed a significant negative correlation with EVP, and the El Niño 3.4 (N3.4) and East Atlantic/Western Russia (EA/WR) indices revealed positive correlations in most regions from 1961 to 1993, while the North Atlantic oscillation (NAO) was negatively correlated with EVP. Moreover, N3.4 and Atlantic multidecadal oscillation (AMO) were positively correlated with EVP from 1994 to 2017. Finally, the yearly number of heatwave events (HWN) was highly correlated with EVP because of the high VPD and SSD levels during the heatwave event periods.

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The canopy layer urban heat island (CLUHI) and surface urban heat island (SUHI) refer to higher canopy layer and land surface temperatures in urban areas than in rural areas, respectively. The long-term trends of CLUHIs are poorly understood at the regional scale. In this study, 1 km resolution air temperature (Ta) data for the 2001-2018 period in the mainland of China were mapped using satellite data and station-based Ta data. Subsequently, the temporal trends of the CLUHI and SUHI intensities (CLUHII and SUHII, respectively) were investigated in 272 cities in the mainland of China. The Ta was estimated with high accuracy, with a root mean square error ranging from 0.370 °C to 0.592 °C. The CLUHII and SUHII increased significantly in over half of the cities in spring and summer, over one-third of the cities in autumn, and over one-fifth of the cities in winter. The trends of the nighttime SUHII were strongly related to the CLUHII calculated using mean and minimum Ta (correlation coefficients ranging from 0.613 to 0.770), whereas the relationships between the trends of the daytime SUHII and CLUHII were relatively weak. Human activities were the major driving forces for the increase in the CLUHII and SUHII. The difference in impervious surfaces between urban and rural areas was significantly correlated with the CLUHII and SUHII in approximately half of the cities. Meteorological factors were significantly correlated with the CLUHII and SUHII in few cities. This study highlights the trends of the significant increase in the CLUHII and SUHII in the mainland of China, which may have negative effects on humans and the environment.

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The spatial, diurnal and seasonal variations of surface urban heat islands (SUHIs) have been investigated in many places, but we still have limited understanding of the interannual variations of SUHIs and associated drivers. In this study, the interannual variations in SUHI intensity (SUHII, derived from MODIS land surface temperature (LST) data (8-day composites of twice-daily observations), urban LST minus rural) and their relationships with climate variability and urbanization were analyzed in 31 cities in China for the period 2001-2015. Significant increasing trends of SUHII were observed in 71.0%, 58.1%, 25.8% and 54.8% the cities in summer days (SDs), summer nights (SNs), winter days (WDs) and winter nights (WNs), respectively. Pearson's correlation analyses were first performed from a temporal perspective, which were different from a spatial perspective as previous studies. The results showed that the SUHII in SDs and WDs was negatively correlated with the background LST and mean air temperature in most of the cities. The nighttime SUHII in most cities was negatively and positively correlated with total precipitation and total sunshine duration, respectively. Average wind speed has little effect on SUHII. Decreasing vegetation and increased population were the main factors that contributed to the increased SUHII in SDs and SNs, while albedo only influenced the SUHII in WDs. In addition, Pearson's correlation analyses across cities showed that cities with higher decreasing rates of vegetation exhibited higher increasing rates of the SUHII in SDs and WDs. Cities with larger population growth rates do not necessarily have higher increasing rates of SUHII.

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In this study, the relationships between interannual variations of surface urban heat islands (SUHIs) and climate variability were studied in 31 cities of China for the period 2001-2016. For cold and dry Northern China, it was found that the interannual variations of SUHI intensity (SUHII, land surface temperature (LST) in urban minus rural) in urban cores was significantly (p<0.05) and negatively correlated with rural LST in 9 (in summer days (SDs)) and 8 (in winter days (WDs)) of the 15 northern cities, respectively. In addition, the daytime LST differences between hot summers and other summers and between cold winters and other winters were generally lower in urban cores (1.141°C for SDs and 2.535°C for WDs) than in rural areas (1.890°C for SDs and 3.377°C for WDs). The standard deviation was further used to reflect the interannual stabilities of LST, enhanced vegetation index (EVI) and white sky albedo (WSA). Interestingly, the standard deviations of LST across 2001-2016 were generally lower in urban cores (0.994°C for SDs and 1.577°C for WDs) than in rural areas (1.431°C for SDs and 2.077°C for WDs). Similar results were observed for EVI and WSA (winter). The results suggested that the urban surface is less sensitive to climate variability than rural areas in Northern China. Comparatively, most findings were less evident in hot and humid Southern China. Despite the whole world would become warmer or colder in future, the insensitivity of urban surface may mitigate its impacts in cold and dry Northern China. However, it does not mean that urbanization is totally good due to its environmental problem.

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There are many studies focusing on spatial variations of surface urban heat islands (SUHIs) in literature. In this study, MODIS land surface temperature (LST) data and China's Land Use/Cover Datasets (CLUDs) were used to examine the temporal trends of SUHIs in 31 major Chinese cities during 2001-2015 using three indicators: SUHI intensity (SUHII), area of the SUHI (AreaSUHI) and percentage of area with increasing SUHII (PAISUHII). Correlation analyses between SUHII and background (rural) LST (extracted from MODIS LST), vegetation coverage (reflected by MODIS EVI data) and anthropogenic heat release (reflected by nighttime light data) were performed from temporal rather than spatial perspectives. Our findings showed that the SUHII and AreaSUHI in urbanized areas increased significantly in most cities in summer days, whereas they increased significantly in approximately half and more than half of the cities in summer and winter nights, respectively. In summer days, summer nights and winter nights, the PAISUHII was approximately 80% and over 50% in union areas and the 20km buffer, respectively. Correlation analyses indicated that the SUHII in stable urban areas was negatively correlated with the background LST in summer and winter days for most cities, especially in northern China. A reduction in vegetation contributed to the increasing SUHII in urbanized areas in summer days and nights. The increasing anthropogenic heat release was an important factor for increases in the SUHII in urbanized areas.