Based on the 30 km CWRF regional climate model, a new model was developed with a horizontal resolution further refined to 15 km. ERA-Interim atmospheric reanalysis data from the European Center for Medium Range Forecasts and OISSTv2 sea surface temperature data from the United States were used to drive two CWRF models. Compared with the observed data of China’s homogenized air temperature dataset CN05.1, the simulation effect of CWRF models on 2 m-air temperature over China during 1982-2016 and its sensitivity to horizontal resolution were systematically analyzed. The results show that simply modifying the underlying surface information of the model has little effect on the simulation results without improving the model resolution, the 15 km resolution CWRF model has a better simulation effect on the temperature in the complex terrain area, and has a better performance on the spatial distribution, inter-annual variation and extreme temperature simulation. From the perspective of climate mean temperature distribution, the 15 km model reduces the cold deviation to less than 1℃ in spring, autumn and winter over Southwest China and Qinghai-Tibet Plateau, and Southwest, South China and Central China in summer, where the cold deviation is more significant simulated by the 30 km CWRF model. From the perspective of inter-annual temperature variation, the simulation results of 15 km model over Central China, North China and southern Northeast China in summer and winter are better than those of 30 km model, and the correlation coefficient increases by 0.4 at the highest. In terms of the simulation of extreme events, compared with the 30 km model, the 15 km model has a great improvement in the simulation of the maximum number of days with the annual daily maximum temperature >25℃ from South China to Fujian, Jiangxi, Hunan and eastern Hubei, the root mean square error decreases by 1 d, and the extreme high and low temperature in eastern Xinjiang, Northeast China, the Qinghai-Tibet Plateau and South China are also significantly improved, the root mean square error is reduced by 1℃. Therefore, higher resolution regional climate models are helpful to improve the precision simulation ability of temperature in China.

Based on the historical documents the 1632 North China rain and flood event was reconstructed with the rainfall, water distribution and disaster. The spatial and temporal extent of the heavy rainfall event in Henan province in August 1632 was identified and compared with the extreme precipitation event in Henan province in 2021, which is supported by instrumental records. The large-scale precipitation in North China in 1632 began on July 17th. From June 18th to November 11th, there were records of prolonged rainfall in different areas of the entire North China region. This prolonged precipitation, along with several instances of heavy rainfall, resulted in severe flooding in the Huanghuai region. The flooding was so severe that it caused the Yellow River to overflow and collapse, leading to significant socio-economic impacts. Through analyses of the precipitation and flooding process, local precipitation and water brought by external sources were differentiated. In 1632, the eastern monsoon region of China experienced a drought-flood pattern, with partial flooding in southern and northern China and partial drought in the lower reaches of the Yangtze River. A comparison between the torrential rainfall and flooding in North China in 1632 and the extreme precipitation event in Henan in July 2021 revealed several similarities. Both events saw heavy precipitation concentrated along the eastern foothills of the Taihang Mountains and the east side of the Funiu Mountains, influenced by topography. In 2021, the center of heavy precipitation was further north. The extreme precipitation in Henan in 2021 was linked to Typhoon “Fireworks”, with similar typhoon activity recorded in Jiangsu and Zhejiang before and after the 1632 event. Both years were affected by pre-La Niña events, leading to flooding in North China. Moving forward, it is crucial to focus on La Niña and typhoon events, enhance heavy rainfall forecasting and disaster warning capabilities, and prioritize urban flood prevention to prevent events like the “7·20” Zhengzhou urban flooding. These findings can inform future flood control strategies.

This article reviews the studies on the characteristics, causes, and impacts of climate change over Tibetan Plateau (TP) in recent 20 years, particularly in recent 10 years. Since 1960, the overall temperature over the TP has significantly increased and there is a significant altitude dependence. Greenhouse gases, feedback of ice and snow albedo, and of cloud-water vapor-radiation, as well as the local forcing are important factors affecting the altitude dependence of the TP warming. The precipitation on the TP shows an overall increasing trend, and shows stronger regional and seasonal differences than surface air temperature changes. The spatial variation of precipitation is mainly divided into north-south dipole, east-west dipole, central and marginal difference type, and multivariate type. The most significant precipitation increase occurs in summer. Affected by climate change and anthropogenic aerosol emissions, the water resources especially cryosphere water resources over the TP have undergone drastic changes, including accelerated glacier shrinkage, increased glacier runoff, severe lake expansion, leading to climate warming and humidification. The changes in snow cover on the TP have shown significantly interdecadal variations. Finally, further research and policy recommendations are proposed.

The community Earth system model (CESM) was used to investigate the climate response to CO₂ radiative forcing that is associated with CO₂-induced changes in atmospheric radiation, and physiological forcing that is associated with CO₂-induced changes in stomatal opening. The results show that both forcings cause global surface warming. During the fast adjustment processes, CO₂-physiological forcing contributes to (27.5±0.9)% of the total global land mean warming. The effects of the two forcings on the global water cycle are significantly different. As a result of CO₂ doubling, at equilibrium state, the CO₂ radiation forcing increases global surface evapotranspiration by (5.2±0.1)% and runoff by (8.0±0.4)%, CO₂ physiological forcing decreased global surface evapotranspiration by (3.9±0.1)% and increased global runoff by (10.1±0.4)%. With the increase of CO₂ concentration, under the influence of radiative forcing, surface temperature, evapotranspiration and precipitation continue to increase, but the increase rates slow down. Under the influence of physiological forcing, surface evapotranspiration continues to decrease, and the decreasing rate do not change significantly. The changes caused by the combined effect of two types of CO₂ forcings are different from the sum of changes caused by the two forcings alone, and this difference becomes more and more significant with the increase of CO₂ concentration.

Runoff into the sea is an important link in the water cycle. Exploring the spatial and temporal variation characteristics of global runoff into the sea under the background of climate change can provide a basis for the rational utilization of water resources. Based on the monthly discharge of 376 outflow rivers around the world, ERA5-LAND reanalysis data and 10 global climate models, a precipitation-runoff relationship model based on the SSA-BP neural network was constructed to analyze the spatiotemporal change characteristics of the global runoff into the sea during the historical period (1961-2020) and the future (2021-2100) under three scenarios (SSP1-2.6, SSP3-7.0 and SSP5-8.5). The results are as follows. (1) On a global scale, from 1961 to 2020, the multi-year average annual runoff into the sea was 37423 km³. From 2021 to 2100, the global annual runoff into the sea will show an increasing trend under the three future scenarios, with a significant trend under the SSP1-2.6 scenario. Compared with the base period, the late 21st century showed the largest increase. (2) On an intercontinental scale, during historical periods, Africa’s runoff into the sea showed a significant decreasing trend, while North America showed a significant increasing trend. From 2021 to 2100, Asia and North America will show an increasing trend under three scenarios, Oceania will show a decreasing trend, and there are obvious differences among the scenarios for the remaining continents. (3) In terms of latitudinal distribution, the change trend in the low latitudes of the Northern and Southern Hemispheres during the historical period was not significant; the mid-latitude Northern Hemisphere showed a weak decreasing trend, the Southern Hemisphere showed a significant decreasing trend; and the high latitudes of the Northern Hemisphere showed a significant increasing trend. From 2021 to 2100, from low to high emission scenarios, the increasing trend of runoff into the sea in the low latitudes of the Northern Hemisphere and the decreasing trend in the Southern Hemisphere become more and more significant; in the mid-to-high latitudes of the Northern Hemisphere, the significant increase in the low emission scenario changes to the significant decrease in the medium and high emission scenario; The mid-latitudes of the Southern Hemisphere show a significant increasing trend under the low emission scenario, but the trend is not significant under the medium and high emission scenario.

In order to investigate the impact of climate adaptation technology adoption on farmer households’ agricultural income under the impact of climate change, the income-growth effect of climate adaptation technology adoption on farmer households and the mechanism of the effect on income gap were analyzed with different levels of agricultural income based on 2502 research data of farmer households in six provinces in the middle and upper reaches of the Yellow River, using the Unconditional Quartile Regression (UQR) and Recentered Influence Function (RIF) decomposition regression. Conclusions are as follows. Climate adaptation technology adoption has decreasing effect on the income increase of farm households from low to high levels of agricultural income, and the effect on the income increase of farm households with low level of agricultural income is the most obvious. Climate adaptation technology adoption can narrow the gap of agricultural income within the farm households, and the effect is the most obvious within the low level agricultural income group. The RIF decomposition regression shows that the structural effect is the main reason for the narrowing of the gap of agricultural income within the farm households, mainly with the effect of education level of farm households on the income gap. Therefore, climate adaptive technologies should be widely promoted, and at the same time, attention should be paid to improve the understanding and cognition of farmers on climate adaptive technologies, improve the willingness of farmers to accept climate adaptive technologies, encourage the low-income farmers to adopt a combination of climate adaptive technologies, and the government should give appropriate subsidies to reduce the burden of farmers.

While large-scale foreign investment is seen as a participant in China’s economic growth miracle, it is also seen as an important contributor to China’s carbon emissions. In 2020, China made a solemn commitment to the world: strive to peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060. In the context of the continuous compression of the negative list of foreign investment access, how will the further introduction of foreign investment affect the realization of the “dual carbon” goal? In order to answer this question, based on the carbon dioxide emissions data at the provincial and industry level from 2001 to 2019, combined with the Catalogue of Industries for Foreign Investment Guidance and the Catalogue of Industries with Advantages for Foreign Investment in Central and Western Regions, a DID model was constructed to study the carbon emission effect, mechanism of action, heterogeneity and the relationship between foreign investment access policy and carbon peaking. The results show that the foreign investment access policy significantly reduces the level of carbon emission, and the policy mainly achieves carbon emission reduction by promoting the clean energy structure. Foreign investment access policy has no significant impact on carbon emissions in the eastern region, significantly inhibits carbon emissions in the central and northern regions, and significantly promotes carbon emissions in the western and southern regions. Compared with high-carbon emission industries, this policy has a more prominent effect on carbon emission reduction in non-high-carbon emission industries. Further analysis shows that there is an “inverted U-shaped” curve relationship between the scale of foreign investment and carbon emissions, and most industries in most provinces in China have not yet crossed the inflection point. Therefore, it is suggested that the government in the future further relax the foreign investment access policy, continue to promote the optimization of clean energy structure, and formulate differentiated “negative lists” according to regional and industry heterogeneity to promote the realization of the “dual carbon” goal.

The entire lifecycle carbon reduction of buildings has been widely applied and developed in Japan. Due to the similar regional characteristics, climate conditions, and cultural environment between China and Japan, the comparative analysis of building carbon emissions in China and Japan has high reference significance and value for the low-carbon development of buildings in China. However, due to the differences in the statistical caliber of carbon emissions throughout the entire lifecycle of buildings between China and Japan, it has brought difficulties to accurately carry out comparative analysis of carbon emissions in Chinese and Japanese buildings. In this study, an entire lifecycle carbon emission calculation model with the same caliber as China and Japan was first established from the aspects of calculation boundaries, statistical accuracy of building materials, and number of carbon emission factors. Based on this, a comparison and analysis of the entire lifecycle carbon emission characteristics of Chinese and Japanese building cases was conducted. It was found that the carbon emissions throughout the entire lifecycle of Chinese construction cases were slightly higher than those of Japan, especially during the operational phase, where carbon emissions were 43.52% higher than Japan. In addition, the performance of building materials has a significant impact on carbon emissions. Japanese buildings use low-carbon building materials to produce low carbon emissions and have a long service life, resulting in lower carbon emissions during maintenance and disposal compared to Chinese construction cases.

Urban agglomerations are the agglomerations of China’s economic development and energy consumption, as well as the main source of carbon emissions. The study of the spatial-temporal evolution characteristics and driving factors of carbon emissions in China’s typical urban agglomerations is of great significance for the achievement of carbon peaking and carbon neutrality goals. The carbon emission driving factors (population size, economic level, industrial structure, energy intensity and energy structure) of Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Chengdu-Chongqing urban agglomerations were analyzed by using ST-IDA model and LMDI decomposition method. The results are as follows. During 2000-2019, the overall trend of CO₂ emissions from energy activities in the four major urban agglomerations stepped from the “high growth stage” to the “stable growth stage”, among which the Chengdu-Chongqing urban agglomeration have basically achieved the carbon peak; The energy intensity effect is the main factor affecting the spatial differences in carbon emissions; The expansion of population size, the increase of economic development level and the rise of energy intensity are the main factors contributing to the growth of carbon emissions in the four major urban agglomerations, while the optimization of industrial structure and energy consumption structure plays a suppressive role; The spatial and temporal evolution of carbon emissions in the four major urban agglomerations depends mainly on the industrial sector. Since the four major urban agglomerations present different carbon emission characteristics, differentiated and diversified emission reduction paths should be explored in the future to promote carbon emission reduction in urban agglomerations.

China has emphasized the need to promote synergy in reducing pollution and carbon emissions as the overall starting point for promoting the comprehensive green transformation of economic and social development, and has put forward the goal of “effectively improving the synergy of pollution reduction and carbon reduction by 2025”. Therefore, there is an urgent need to carry out research on the evaluation of the synergy degree of pollution reduction and carbon reduction. Based on the analysis of the connotation of urban pollution reduction and carbon reduction synergy, an evaluation index system of urban pollution reduction and carbon reduction synergy was constructed, including 3 first-level indicators, 13 second-level indicators and 22 third-level indicators. At the same time, Beijing, Chongqing, Tangshan and Xining, which represent service-oriented, comprehensive, industrial and ecological priority cities, respectively, were selected to carry out the application research on the evaluation index system of urban pollution reduction and carbon reduction synergy. The results show that the evaluation index system of urban pollution reduction and carbon reduction synergy constructed in this paper is universal and operable, and the evaluation results are of practical significance. It is suggested that this indicator system should be used as an important reference for the national pilot work to promote the collaborative innovation of urban pollution reduction and carbon reduction and the evaluation of the synergy degree of pollution and carbon reduction in cities.

The 28th Conference of the Parties (COP28) to the United Nations Framework Convention on Climate Change (UNFCCC) has completed the first Global Stocktake under the Paris Agreement, and has reached a package of key decisions dubbed the United Arab Emirates (UAE) Consensus. The Global Stocktake outcome upholds the effectiveness of the Paris Agreement mechanism, highlights the urgency of strengthening global mitigation actions with the 1.5°C temperature target, decides the framework for the global goal on adaptation, establishes loss and damage fund, emphasizes the issue of climate finance support gap, and clarifies the concept of climate finance. China has made important contributions to the achievement of the Global Stocktake outcome in terms of leadership diplomacy, climate governance concepts and climate change negotiations. The global climate multilateral process has shown following trends with the first Global Stocktake. The global climate governance has gradually become multi-channel, multi-disciplinary and target-oriented. Geopolitics has exacerbated green trade barriers. Developing countries’ positions are increasingly fragmented. Countries will take climate action in the mode of “NDC+” under the Paris Agreement, and continuously update and enhance their Nationally Determined Contributions (NDCs) in accordance with new requirements. It is recommended that China need further take into account both the domestic and international imperatives, grasp the trend of global green development, actively fulfill our obligations under the UNFCCC and Paris Agreement, strengthen the climate change-related research and capacity building, and make holistic planning and coordination for COP29 in advance.