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Accurate prediction of regional terrestrial water storage change (TWSA) is of great significance for water resources planning and management, and early warning of extreme climate disasters. Aiming at the problem that the conventional methods on prediction of TWSA time series are difficult to be accurate, the six typical regions are selected in China as examples, including the upper reaches of the Yangtze River (UYR), the southwest region (SWR), the Liaohe River Basin (LRB), the North China Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl River Basin (PRB). The mascon product from GRACE/GRACE-FO provided by CSR is used to extract TWSA time series in six typical areas. The improved Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) neural network and the latest Bidirectional LSTM (BiLSTM-attention) neural network model based on attention mechanism are proposed to predict and analyze the regional TWSA. In the experiment, the selection of the optimal model parameters such as the number of hidden layer nodes and the number of hidden units of the neural network model is tested and analyzed in detail. Meanwhile, the model prediction results are compared with the traditional least squares method and random forest (RF) prediction method. The root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) were used to evaluate the accuracy of the predicted results. The results show that the improved BP, LSTM and Bi-LSTM-attention neural network models all achieve higher prediction accuracy in UYR and SWR areas. RMSE is less than 2.641 cm, R2 is as high as 0.8 or more, NSE is above 0.6, and MAPE is within 0.1. Compared with the least square method, the RMSE of the predicted results from three neural network decreased by 0.998 cm, 0.700 cm and 0.7563 on average, and the R2 increased by 81.75%, 69.89% and 72% on average. Compared with RFML method, the RMSE from three neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and R2 is increased by 38.20%, 24.60% and 27.06% on average. NSE and RMSE are improved to varying degrees in the above regions. It shows that the improved BP, LSTM and BiLSTM-attention model used can effectively predict TWSA. The research methods and results in this paper can provide important reference for the rational utilization of regional water resources and disaster risk assessment. [ABSTRACT FROM AUTHOR]

Nowadays, frequent climate extremes exert a serious impact on agricultural production and social development, which is seldom studied in the previous literature. Production–Living–Ecological (PLE) is a useful analysis framework, and China is a suitable model for such study. This paper takes the Huai River Eco-Economic Belt (HREB), an important agricultural zone in China, to study the relationship among agricultural production (P), society (L), and climate change (E), which is referred to as APLE. This paper constructs a coupled coordination evaluation index system for the APLE system and uses coupling coordination degree models and geographic detector to study the spatial and temporal evolution of the coordinated development of 34 counties (cities) in the HREB from 2009 to 2018. The results show the following: (1) The development of the agricultural subsystem and the social subsystem formed a "scissors difference" from 2009 to 2014, and the three subsystems showed a slight upward trend during 2014–2018. (2) The coupling and coordinated development of the APLE system in the HREB was generally stable, and the coupling coordination degree was improved from low-grade and slightly uncoordinated to barely and primarily coordinated. Furthermore, the spatial differentiation of the coupling coordination degree shows a clear pattern of being high in the southeast and low in the northwest. (3) The main influencing factors are the drought and flood protection rate, the effective irrigation rate, the per capita electricity consumption in agriculture, the number of beds in healthcare facilities per 10,000 people, the per capita disposable income of urban residents, the annual average temperature, and the annual precipitation. (4) The spatial–temporal evolution of the coupling and coordinated development of the APLE system is the result of the comprehensive effect of internal driving forces such as food security, the consumption level of rural residents, and the development level of urbanization construction, and external driving forces such as government public welfare and natural conditions. [ABSTRACT FROM AUTHOR]

In the context of an increasingly extreme climate, Urban Heat Island (UHI) mitigation of communities through ventilation has recently attracted more attention. To explore the impact mechanisms of different morphological renovation schemes on its wind and thermal environment, this paper selected the Laozheng Community as a case study and: (1) analyzed measured data to quantitatively investigate the UHI within the community; (2) established the CIM-WTEPS system to construct community information models and to conduct wind environment parametric simulation for seven micro-renovation schemes across three levels; (3) performed correlation analyses between morphology indicators and wind environment indicators; (4) conducted the thermal environment parametric simulation of the community under different schemes. The results reveal that: (1) the Laozheng Community exhibits the Urban Heat Island Intensity (UHII) of up to 6 °C; (2) apart from the " Hollowing " scheme, which deteriorates the community wind environment, all other schemes optimize it, potentially increasing the average wind speed by up to 0.03m/s and in the renovated area by up to 0.42 m/s; (3) building density is highly correlated with the average wind speed and the proportion of calm wind area, with correlation coefficients of −0.916 (p < 0.01) and 0.894 (p < 0.01), respectively; (4) the adding of shading facilities can enhance the proportion of areas with lower Universal Thermal Climate Index (UTCI) without adversely affecting the optimization effects of the wind environment, achieving an maximum increase of 3.1%. This study provides a reference for optimizing the community's microclimate through morphological micro-renovations and detailed operations, aiding designers in better controlling community morphology for in future community renewal and design planning, thereby creating a more hospitable outdoor environment. [ABSTRACT FROM AUTHOR]

Food is the material basis for human survival. Therefore, food security is a top priority for the people's livelihood and the sustainable development and future destiny of human beings. In the context of global warming in recent decades, the Arctic region has experienced more significant temperature anomalies than the midlatitudes due to the "Arctic amplification," and the rate of sea ice reduction has accelerated, which has an important impact on climate change in the middle and high latitudes, especially the frequent occurrence of extreme climate disasters that seriously affect food security and China's agricultural production. However, little research has been conducted on the role of changes in this important system of Arctic sea ice in China's agricultural production. Therefore, this paper analyzes the interannual variability and multi-year trends of Arctic sea ice concentration, CO2, air temperature, precipitation and China's major crop yield data to explore the possible effects and mechanisms of the rapid decrease in Arctic sea ice on China's grain production. From the analysis, it was found that the yield of major grains (rice, maize, wheat and soybean) in China was closely related to the Arctic sea ice anomaly in the previous summer and autumn, and the influence process was primarily through the dynamic process of the Arctic sea ice anomaly affecting the meridional temperature gradient and the positive and negative Arctic Oscillation phases, which in turn affected the air temperature anomalies in Eurasia and China, and finally led to the anomalous changes in Chinese grain yield. Based on this, a prediction model of China's major grain yield was established by stepwise nonlinear multiple regression analysis, which is a good fit and is expected to increase China's major crop yield by 11.4% in 2022 compared with last year. This presents new ideas and methods for future grain yield assessment in China and has far-reaching guidance for the stability and development of national and regional economies worldwide. [ABSTRACT FROM AUTHOR]

In recent years, concurrent climate extreme conditions (i.e., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to evaluate the impact of global warming on ultra‐extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot‐wet events with concurrence in the same month and year would be expected for China under the continuously‐warming scenarios. This is particularly obvious for the west where more than 6 hot‐wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra‐extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot‐wet and cold‐wet extremes concurring in the same month will be larger than those of dry‐related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Plain Language Summary: In recent years, concurrent climate extreme conditions (e.g., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Here, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to address this question. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. Both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. Overall, the results suggest that slowing down the global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Key Points: A downscaled and bias‐corrected CMIP6 ensemble is used to evaluate the impact of global warming on ultra extreme events over ChinaMore frequent extreme hot‐wet events with concurrence in the same month and year would be expected for ChinaBoth extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level [ABSTRACT FROM AUTHOR]

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Recent years have witnessed climate change characterized by increasingly frequent extreme precipitation events, and the assumption of stationarity in traditional frequency analyses is gradually being questioned. In terms of the current research status in China, there is a lack of thorough investigations on the linkage between extreme precipitation and climate change. This paper aims to determine the dominant climate indices as well as the corresponding significant time scales and periods affecting extreme precipitation over China for dynamic assessments of the upcoming rainstorm risk. Correlations between 15 climate indices and precipitation extremes, as well as the correlations among climate indices, are fully explored to identify potential predictors for non-stationary modeling. Then, 21 non-stationary generalized extreme value (GEV) models are constructed, and the optimal covariates as well as their lag times with extreme precipitation at 769 stations are ascertained in a Bayesian framework. Finally, a complete predictive process is developed, and the national rainstorm risk under non-stationary conditions is assessed. The results indicate that precipitation extremes remain stationary only at 74 stations (less than 10%). WPI is dominant in modeling the variability in precipitation extremes for nearly 22% of the total stations, ranking first among all the climate indices. The predominant time scale affecting extreme precipitation at the majority of stations is 3 months. Ignoring the non-stationarity of extreme precipitation inevitably leads to misperceptions of rainstorm risks, and the spatial distribution of the maximum case of the design rainstorms under non-stationary conditions differs remarkably from that under stationary conditions. Our findings have important implications for the in-depth understanding of the real drivers of extreme precipitation non-stationary and enable advanced predictions of rainstorm risks for mitigating subsequent disasters. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since land use/cover change profoundly impacts climate change and global warming has become an irreversible trend in the Anthropocene, there have been numerous global studies on the impact of climate change on vegetation growth (VG). However, the effects of climate extremes on the growth and direction of various vegetation types need to be better investigated, especially in the climate transition zones. In this paper, we examined the effect of diurnal warming on the growth of various types of vegetation in China's north–south transition zone. Based on the daily observation data of 92 meteorological stations in the Qinling-Daba (Qinba) mountainous area from 1982 to 2015, coupled with the Normalized Difference Vegetation Index (NDVI) and data on the type of vegetation. This research examined the temporal changes in the highest and lowest temperatures during the last 33 years using trend analysis. Second-order correlation analysis was used to investigate vegetation NDVI response characteristics to diurnal warming and to examine the effect of diurnal warming on the growth of different vegetation types. Our results showed that maximum temperature (Tmax) and minimum temperature (Tmin) showed an obvious upward trend, with the daytime temperature increase rate 1.2 times that at night, but failing the t-test. In addition, diurnal warming promoted vegetation growth, with NDVI associated positively correlated with Tmax at approximately 91.2% of the sites and 3492 rasters and with Tmin at roughly 53.25% of the sites and 2864 rasters. Spatial significance analysis showed an apparent difference, but few areas passed the t-test. Furthermore, daytime warming enhanced the growth of grasses, shrubs, deciduous broad-leaved forests, crops, and conifers, while the effect of nighttime warming on VG had a positive effect only on the growth of evergreen broad-leaved forest vegetation. These findings reveal the mechanisms of the impact of climate extremes on VG under global change, particularly the extent to which different vegetation types in climatic transitional zones respond to climate extremes. [ABSTRACT FROM AUTHOR]

Understanding how crop phenology responds to climate change is critical for enabling agricultural adaptation measures. Pre‐season temperature alone leads to well‐understood changes in crop phenology. Nevertheless, the modulation effect of concurrent precipitation extremes on the response to temperature extremes has been largely under‐addressed. Here, we investigate the response of rice transplanting dates to pre‐season temperature extremes and reveal the modulation effects of concurrent precipitation extremes by using station‐observed rice transplanting dates from 1981 to 2018 across mainland China. We also evaluate the performance of a remotely sensed phenology product, ChinaCropPhen1km, in reproducing the above temperature responses and modulation effects. Our results showed that transplanting dates tended to advance in response to an extremely hot pre‐season, while concurrent extreme drought offset the advance by up to 2.6 days. Transplanting dates tended to be delayed in response to an extremely cold pre‐season, while concurrent extreme precipitation exacerbated the delay by up to 1 day. Responses to temperature extremes and modulation effects were divergent across different regions. Under extremely hot conditions, transplanting dates advanced further in hotter and wetter regions, while under extremely cold pre‐seasons, transplanting dates delayed less in colder and drier regions. Transplanting dates from the ChinaCropPhen1km product underestimated the responses to temperature extremes by up to 4.7 days and detected the opposite modulation effect compared to those obtained from observations. Our results highlight that the need to improve our understanding and modeling of modulation effects of precipitation extremes on temperature–phenology relationship, which benefits the field of agriculture risk analysis and climate change adaptation. Plain Language Summary: Frequent climate extremes are projected with ongoing climate change. This paper reveals the effect of concurrent precipitation extremes in modulating rice transplanting date response to pre‐season temperature extremes in China and assesses remotely sensed transplanting dates from the ChinaCropPhen1km product in reproducing the above temperature responses and modulation effects. Results show that concurrent precipitation extremes substantially influence the temperature–transplanting date relationship. Two kinds of modulation effects are detected: (a) concurrent extreme precipitation tends to exacerbate the extremely cold‐induced delay by up to 1 day, and (b) concurrent extreme drought conditions tend to offset the extremely hot‐induced advance by up to 2.6 days. From a spatial perspective, responses to temperature extremes and modulation effects are divergent across regions. Additionally, remotely sensed transplanting dates from the ChinaCropPhen1km product underestimate the temperature responses and detect the opposite modulation effects compared to observations. This study highlights the critical role of concurrent precipitation extremes in modulating temperature impact on crop phenology, which requires more understanding and modeling in future analysis. Key Points: Concurrent extreme drought offset the extreme hot induced‐advance, and extreme precipitation exacerbate the extreme cold induced‐delayResponses to temperature extremes and modulation effects of precipitation extremes are divergent across regionsRemotely sensed transplanting dates from ChinaCropPhen1km underestimate the responses to temperature extremes and modulation effects [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB) is an important strategic area in China. However, frequent extreme precipitation events have caused great economic losses and human casualties in this region. In this article, we explore the spatial and temporal links between extreme precipitation events and Sunspot Number (SSN), El Niño Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) in this important economic belt. According to the research findings, all of the extreme precipitation indices (EPIs) except for consecutive dry days (CDD) and consecutive wet days (CWD) showed an upward trend in the YREB over the 59 years. The spatial distributions of very wet days (R95p), extremely wet days (R99p), max 1-day precipitation amount (Rx1day) and max 5-day precipitation amount (Rx5day) had similar distribution patterns, showing decreasing trends from east to west. The EPIs generally had a 2–4-year band, suggesting stronger and more elusive changes. The wavelet coherence (WTC) spectra suggested that SSN, ENSO, AO, and PDO have different effects on extreme precipitation events during different time periods. Before 1985, the SSN, ENSO, AO, PDO and extreme precipitation events shared similar oscillation periods, but after 1985, their oscillation periods were no longer consistent with each other. In addition, solar activity and the AO mainly had negative correlations with extreme precipitation events, while the ENSO and PDO had predominantly positive correlations with the EPIs. This paper provides a reference for national economic strategic planning and natural resource management in the YREB. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

The cold surge (CS) is an extreme climate event that frequently affects China. By using a 0.25° × 0.25° gridded observation dataset provided by the China Meteorological Administration (CN05.1), this paper investigates the spatial–temporal characteristics of various CSs in China during 1962–2018. According to the duration and coverage area (CA), CSs are classified into the short‐lived cold surges (SLCSs), the persistent cold surges (PCSs) and the extensive and persistent cold surges (EPCSs). They are also divided into the weak cold surges (WCSs), the strong cold surges (SCSs) and the super strong cold surges (SSCSs) based on the temperature drop (ΔT). The extreme cold surges (ECSs) are also defined and occur 38 times over 57 years. The total CSs and various CSs all display patterns of higher frequencies in the northeast and lower frequencies in the southwest. Northeast China and Inner Mongolia are frequently hit by various CSs, but the frequencies show overall downward trends in the regions during 1962–2018. The ΔT of the total CSs and ECSs both show significant decreasing trends in the whole of China from 1962 to 2018. From 1962 to 2018, the annual frequency and the ΔT of the total CSs both show decreasing trends. SLCSs and WCSs frequencies show overall increasing trends, but other kinds of CSs show overall decreasing trends. The annual frequencies of the total CSs and various CSs all have trend abrupt changes around 2000. The ΔT of ECSs shows a sharply increasing trend during 1962–2018. The total CSs and all kinds of CSs frequencies have a significant periodic oscillation. ECSs occur most in February. The total CSs and other kinds of CSs occur most in January. There are obvious differences between the multiyear average ECSs frequencies before 2000 and after 2000. [ABSTRACT FROM AUTHOR]

This paper presents an integrated study on the effects of human activity, climate change and crustal movement on the changes of the middle reaches of the Tarim River in China. Remote sensing images and rainfall data from 38 stations from 1970 to 2021 were used for the research. The precise point positioning (PPP) approach was applied to ascertain the crustal movement of the study area using 256 Global Positioning System (GPS) station data of the recent 20 years. The results show that in the past 50 years the studied reaches of the river has moved north at maximum 2940 m or 57.9 m on average, and human activity may have played a dominant role in this change though extreme events of climate may have also taken a part in it. However, before the Anthropocene, its northeast migration is likely associated with crustal movement as the basin bottom inclines to northeast. [ABSTRACT FROM AUTHOR]

Rice is a major staple food grain for more than half of the world's population, and China is the largest rice producer and consumer in the world. In a climate-warming context, the frequency, duration and intensity of heat waves tend to increase, and rice production will be exposed to higher heat damage risks. Understanding the negative impacts of climate change on the rice supply is a critical issue. In this study, a new perspective on agricultural weather index insurance is proposed to investigate the impact of extreme high-temperature events on rice production in South China in the context of climate change. Based on data from meteorological stations in Anhui Province in China from 1961 to 2018 and the projected data from five Global Climate Models under three representative concentration pathway (RCP) scenarios from 2021 to 2099, the spatial–temporal characteristics of heat stress and its influence on rice production were analyzed by employing a weather index insurance model. The interdecadal breakpoints in the trends of the heat stress weather insurance index (HSWI) and the payout from 1961 to 2018 in 1987 were both determined, which are consistent with the more significant global warming since the 1980s. The largest increase after 1987 was found in the southeastern part of the study area. The projected HSWI and the payout increased significantly from 2021 to 2099, and their growth was faster with higher radiative forcing levels. The HSWI values were on average 1.4 times, 3.3 times and 6.1 times higher and the payouts were on average 3.9 times, 9.8 times and 15.0 times higher than the reference values for the near future, mid-future and far future, respectively. The results suggest that a more severe influence of heat damage on rice production will probably happen in the future, and it is vital to develop relevant adaptation strategies for the effects of a warmer climate and heat stress on rice production. This paper provides an alternative way to transform the evaluation of the extreme climate event index into the quantitative estimation of disaster impacts on crop production. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The concept of low impact development (LID) plays a crucial role in rainstorm management and non-point source pollution prevention and control. Sorting and summarizing related research through the knowledge map can objectively present the disciplinary structure, research focus, and research hotspots of the LID research. Based on 2103 LID pieces of literature in Chinese and English included in the web of science (WOS) database and China's integrated knowledge resources system (CNKI) database from 2004 to 2021, this paper aims to perform statistical analysis from three aspects: bibliometrics, keyword hotspot co-occurrence and clustering, and literature co-citation clustering. The obtained results reveal that research on LID-based issues maintains a high degree of enthusiasm in China and abroad, but their corresponding focuses are dissimilar. Foreign research essentially focuses on the environmental field with frequent interdisciplinary phenomena, combining the triple goals of water quality improvement, runoff reduction, and multi-functional expansion, and is committed to solving the impact of uncertain factors on urban stormwater management in extreme climates. Chinese research is mostly aimed at unlocking practical engineering problems, which also leads to the majority of research works in the field of building science and engineering. This is mainly due to a series of water-related problems caused by the change in land use types in China. The researchers have determined the type, quantity, location, and combination of the optimal LID measures by establishing appropriate models, using optimization algorithms, and developing multi-level analysis methods. Although the multi-dimensional results of LID in recent years have greatly expanded the framework paradigm, most of the conducted research works are still biased towards the micro-scale. The present hotspot research considers how to make a macroscopic overall layout and efficiently cooperate with the pipelines network, rivers, and lakes systems to unlock the problems pertinent to urban rainwater and non-point source pollution. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

Taking China's provincial panel data from 2010 to 2020 as an example, this paper verifies that climate change, especially extreme climate change, can destabilize China's financial stability and explores the transmission path of climate risks with meteorological disaster losses as the channel variable. Our empirical results demonstrate that: (a) only after the extreme climate change breaks through a certain threshold, thus causing a significant impact on the financial system; (b) we cannot ignore the improvement of disaster prevention and reduction ability brought by the improvement of social and economic development level; (c) the impact of extreme climate change will spread to the financial system through the economic losses caused by meteorological disasters; (d) there is obvious regional heterogeneity in the spread of meteorological disaster losses to the financial system. Our results are robust to a wider battery of checks and have important policy implications. • This paper empirically tests the impact of climate change on financial stability. • Extreme climate change will damage China's financial stability. • The economic losses caused by climate disasters will spread to the finance. • The transmission of such losses has obvious regional heterogeneity. • This paper provides policy implication for coping with climate risks. [ABSTRACT FROM AUTHOR]

This paper used original survey data in Shandong province of China to depict local farmers' perceptions of combined climatic and market risks, and their adaptive behaviors. Two Logit models were developed to capture the empirical relationship between farmers' risk perception of the combined risks and adaptive behaviors. Results show that farmer's risk perception of climate change and market significantly affected their adoption behavior of adaptive measures, including perception of rainfall decrease, gale decrease, drought increase, and price fall of agricultural products. Moreover farmers' adaptive behaviors are more sensitive to their perceptions of extreme climatic events. Addition of demographic factors can improve the explanatory capacity of the Logit model. As illustrated by the models, male and better educated farmers have greater willingness to take actions for averting risks, while household heads with bigger farm are more reluctant. A majority of respondents chose to construct farm structure for irrigation and water conservation to avert climatic risks. Insurance, which is regarded as an effective tool to help farmers mitigate risks and avoid losses, has not been sufficiently accepted by farmers in rural areas in Shandong province. This underscores the need of effective communication of knowledge to enhance farmers' coping capacity and to encourage their active response to risk. [ABSTRACT FROM AUTHOR]

• The influences of the geometric parameters of the reactor on the SCPP-HPCR are investigated. • A formula is proposed to calculate the CO 2 emissions reduction of the system. • The optimal operating conditions of the SCPP-HPCR is analyzed. • 2.38 kg of CH 4 is degraded and 375.52 kg of CO 2 is reduced through the system one day. The increase of 1.09 °C in global temperature resulted in significant catastrophes because of the extreme climate. The climate change rate can be slowed down by reducing the levels of CH 4 and CO 2 in the atmosphere. The solar chimney power plant integrated with a honeycomb photocatalytic reactor (SCPP-HPCR) uses the photocatalytic technology to remove atmospheric CH 4. In addition, CO 2 emissions can be reduced from coal fired power plant due to the generating capacity of the system. In this paper, the influence of the geometric parameters of the reactor and turbine on the overall performance of the SCPP-HPCR is studied by numerical simulation. The obtained results showed that the flow resistance inside the system was mostly caused by the HPCR at low turbine speed, and primarily by the turbine at high turbine speed. Reducing the pore diameter of the reactor could improve the photocatalytic performance of the SCPP-HPCR more than increasing the turbine speed. The SCPP-HPCR having a pore diameter of 3 mm, porosity of 0.85, and constant turbine speed of 180 rpm, built in Qianyanzhou, China, could remove 2.38 kg of CH 4 and reduce 375.52 kg of CO 2 in one day. [ABSTRACT FROM AUTHOR]

Different geographical zones have regional heterogeneity in underlying earth surface structure and microclimate which result in different evolution trends and their response to climate change varies in extreme rainfalls in these zones. In the Guizhou province of China, there are complex landforms, which lead to spatial redistribution of rainfall, frequent extreme rainfall, and disasters high risk of geologic disasters. Research on extreme climate in Guizhou mostly paid attention to its spatio-temporal characteristics and modeling, but lack of analysis on its characteristics of extreme rainfall variability and response to temperature changes under different subsurface conditions. This study investigated the characteristics of the extreme rainfall spatiotemporal and recurrence periods in Guizhou province and discussed the relationship between the response of extreme rainfall to temperature change. Daily rainfall data from 1990 to 2020 and 2021–2100 at 31 meteorological observation stations throughout the province were collected to calculate extreme precipitation. This research had the following results. (1) Both historical and future periods show an upward trend in extreme rainfall in Guizhou province, with a spatial distribution pattern of "high in the south and low in the north, high in the east and low in the west" and "high in the southeast and low in the northwest", respectively; the spatial distribution of extreme rainfall under each recurrence period is consistent with the non-recurrence period. (2) Both historical and future periods show an upward trend in temperature in Guizhou province, with a spatial distribution consistent with that of the extreme rainfall in the corresponding period. (3) The change in extreme rainfall intensity with increasing temperature is almost always greater than the C–C rate for different periods and underlying earth surface structure; Extreme rainfall has a Hook response structure to temperature change, and the climate response structure shifts to the right with climate warming. The results of the study can provide a basis for decision-making on regional disaster prevention and mitigation in the context of temperature change. [ABSTRACT FROM AUTHOR]

In response to extreme climate change, China has set a goal of reaching emission peak by 2030 and achieving carbon neutrality by 2050. Energy conservation and emission reduction of building heating in northern China are key to achieving this goal. Based on building area prediction and energy consumption intensity, this paper establishes a model for calculating heating energy consumption and carbon emissions in northern residential buildings at the macro level, which provides a basis for formulating policies related to heating and emission reduction in northern buildings. Based on the research method of scenario projection, combining the carbon emission subsets and future heating energy mix projections in northern China, the heating energy consumption in northern China decreases to 175, 149 and 135 Mtce in 2050 under the baseline, medium control and strict control scenarios, respectively. The heating energy consumption in the northern region should be controlled at least under the medium control scheme. Under this scenario, building heating carbon emissions in the north could be reduced to 450 MtCO2 by 2050, or 280 MtCO2 if more stringent abatement technologies are adopted. In order to achieve this goal, a combination of energy-saving technologies must be used. The use of biomass and solar technologies should be emphasized in rural heating, while envelope renovation makes the greatest contribution to heating energy savings, with envelope renovation contributing up to 92 Mtce to urban heating. [ABSTRACT FROM AUTHOR]

This paper presents projections of climate extremes over China under global warming of 1.5, 2, and 3 °C above pre-industrial (1861–1900), based on the latest Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. Results are compared with what produced by the precedent phase of the project, CMIP5. Model evaluation for the reference period (1985–2005) indicates that CMIP6 models outperform their predecessors in CMIP5, especially in simulating precipitation extremes. Areal averages for changes of most indices are found larger in CMIP6 than in CMIP5. The emblematic annual mean temperature, when averaged over the whole of China in CMIP6, increases by 1.49, 2.21, and 3.53 °C (relative to 1985–2005) for 1.5, 2, and 3 °C above-preindustrial global warming levels, while the counterpart in CMIP5 is 1.20, 1.93 and 3.39 °C respectively. Similarly, total precipitation increases by 5.3%, 8.6%, and 16.3% in CMIP6 and by 4.4%, 7.0% and 12.8% in CMIP5, respectively. The spatial distribution of changes for extreme indices is generally consistent in both CMIP5 and CMIP6, but with significantly higher increases in CMIP6 over Northeast and Northwest China for the hottest day temperature, and South China for the coldest night temperature. In the south bank of the Yangtze River, and most regions around 40°N, CMIP6 shows higher increases for both total precipitation and heavy precipitation. The projected difference between CMIP6 and CMIP5 is mainly attributable to the physical upgrading of climate models and largely independent from their emission scenarios. [ABSTRACT FROM AUTHOR]

Rapid global changes are altering regional hydrothermal conditions, especially in ecologically vulnerable areas such as coastal regions, subsequently influencing the dynamics of vegetation growth. However, there is limited research investigating the response of vegetation in these regions to extreme climates and the associated time lag-accumulation relationships. This study utilized a combined approach of gradual and abrupt analysis to examine the spatiotemporal patterns of vegetation dynamics in the coastal provinces of China from 2000 to 2019. Additionally, we evaluated the time lag-accumulation response of vegetation to extreme climate events. The results showed that (1) extreme high temperatures and extreme precipitation had increased over the past two decades, with greater warming observed in high latitudes and concentrated precipitation increases in water-rich southern regions; (2) both gradual and abrupt analyses indicate significant vegetation improvement in coastal provinces; (3) significant lag-accumulation relationships were observed between vegetation and extreme climate in the coastal regions of China, and the time-accumulation effects were stronger than the time lag effects. The accumulation time of extreme temperatures was typically less than one month, and the accumulation time of extreme precipitation was 2–3 months. These findings are important for predicting the growth trend of coastal vegetation, understanding environmental changes, and anticipating ecosystem evolution. [ABSTRACT FROM AUTHOR]

Many agricultural regions in China are likely to become appreciably wetter or drier as the global climate warming increases. However, the impact of these climate change patterns on the intensity of soil greenhouse gas (GHG) emissions (GHGI, GHG emissions per unit of crop yield) has not yet been rigorously assessed. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, we found that climate change is expected to cause a 20.0% crop yield loss, while stimulating soil GHG emissions by 12.2% between 2061 and 2090 in China's agricultural regions. A wetter‐warmer (WW) climate would adversely impact crop yield on an equal basis and lead to a 1.8‐fold‐ increase in GHG emissions relative to those in a drier‐warmer (DW) climate. Without water limitation/excess, extreme heat (an increase of more than 1.5°C in average temperature) during the growing season would amplify 15.7% more yield while simultaneously elevating GHG emissions by 42.5% compared to an increase of below 1.5°C. However, when coupled with extreme drought, it would aggravate crop yield loss by 61.8% without reducing the corresponding GHG emissions. Furthermore, the emission intensity in an extreme WW climate would increase by 22.6% compared to an extreme DW climate. Under this intense WW climate, the use of nitrogen fertilizer would lead to a 37.9% increase in soil GHG emissions without necessarily gaining a corresponding yield advantage compared to a DW climate. These findings suggest that the threat of a wetter‐warmer world to efforts to reduce GHG emissions intensity may be as great as or even greater than that of a drier‐warmer world. Plain Language Summary: Both climate observations and projections suggest that warmer temperatures will intensify convective precipitation, resulting in drier regions becoming drier and wetter regions becoming wetter. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, this study has investigated the effects of drier‐warmer (DW) and wetter‐warmer (WW) climates on crop yield and soil greenhouse gas emissions in China. Our findings indicate that the cost of greenhouse gas emissions in a wetter‐warmer world for crop production could be as high as or even higher than in a drier‐warmer world. Moreover, this study suggests that effective climate mitigation practices need to account for the combined effects of extreme climate changes and nitrogen addition on greenhouse gas emissions and crop production. Therefore, China would need to exert considerably more effort in a future with warmer and wetter conditions to achieve its greenhouse gas emission reduction targets. Key Points: Climate change would cause a 20% crop yield loss and an increase of 12.2% in soil greenhouse gas (GHG) emissions between 2061 and 2090A Wetter‐Warmer climate would cause a more significant crop yield loss and about double the GHG emissions of a Drier‐Warmer climateNitrogen fertilizer‐induced GHG emissions in a Wetter‐Warmer climate would increase 37.9% compared to those in a Drier‐Warmer climate [ABSTRACT FROM AUTHOR]

As extreme climate events become more common with global warming, groundwater is increasingly vital for combating long-term drought and ensuring socio-economic and ecological stability. Currently, the mechanism of meteorological drought propagation to groundwater drought is still not fully understood. This study focuses on the North China Plain (NCP), utilizing statistical theories, spatiotemporal kriging interpolation, and the Mann–Kendall trend test to examine the spatial and temporal distribution characteristics of groundwater from 2005 to 2021. Based on drought theory, the characteristics and propagation process of drought are further quantified. Key findings reveal the following: (1) Shallow groundwater depths in the NCP follow a zonal pattern from the western mountains to the eastern plains and coastal areas. Over two-thirds of this region showed an increase in groundwater depth at a rate of 0–0.05 m/a; (2) Groundwater drought frequency typically ranges from 3 to 6 times, with an average duration of 10 to 30 months and average severity between 10 and 35; (3) Delayed effects last between 0 to 60 months, with attenuation effects varying from 0 to 3 and prolonged effects extending from 0 to 16. Additionally, delayed effects intensify with increasing time scales, while prolonged effects weaken. Notably, both delayed and prolonged effects in the north of the NCP are more pronounced than in the south of the region. This study quantifies the process by which meteorological drought propagates to groundwater drought, offering a new perspective for understanding the interaction between groundwater and meteorological drought. It holds significant scientific importance for monitoring drought and managing water resources in the context of global climate change. [ABSTRACT FROM AUTHOR]

The Report: Intergovernmental Panel on Climate Change (IPCC) set a goal global warming of 1.5°C on global temperature change. Extreme climate changes have increased the demand for electricity consumption by people and enterprises. In fact, China's total power generation in 2019 exceeded 25% of the world's amount, and its thermal power generation accounted for more than 70%. Although past research on electricity efficiency seldom discusses the issue of climate change, the topic still remains important. This research thus considers extreme temperature days (climate change variable) as exogenous variable and uses the Two-Stage Meta Under exogenous undesirable EBM model to examine power efficiency in China. The results are as follows. (1) In the west only Qinghai's GDP, CO2, PM2.5, and electricity consumption have technology gap ratio (TGR) efficiency values of 1 in the 5 years. (2) China's electricity consumption has the same trend with the TGR efficiency of CO2 and is higher than PM2.5. (3) The national overall efficiency, meta overall efficiency, and TGR overall efficiency are the worst in China's west region. [ABSTRACT FROM AUTHOR]

In the context of unprecedented extreme weather and climatic events, the internal structural factors of society play a decisive role in determining the extent to which human beings are affected by disasters and their ability to respond to them. In the past few decades, rapid urbanization in developing countries, such as China, has greatly increased social vulnerability. This process has generated uneven living conditions and created many vulnerable groups, including urban poverty, migrants, and socially and geographically marginalized groups. These groups face difficulties in living conditions, education, livelihood stability, and more. This study sets up indicators from a micro-perspective: three indicators of exposure, four indicators of sensitivity, and eight indicators of adaptive capacity. Based on this evaluation index system, this study conducted a social vulnerability assessment of the population in Hongshan District, Wuhan, China, through individual questionnaire surveys. K -means cluster analysis was used to determine high, medium, and low levels of social vulnerability, which were used to compare different community types and identify vulnerable groups. The results showed close interrelationships between different types of communities in terms of physical and built environments as well as varying levels of social vulnerability to disasters. The high-vulnerability group accounted for 12.9 % of the 599 samples, the medium-vulnerability group accounted for 48.4 %, and the low-vulnerability group accounted for 38.7 %. The higher-vulnerability groups exhibited characteristics such as low education, poor health, low annual income, unstable work, and insufficient social security. Quantitative understanding of the degree of dissimilarity in social vulnerability among different communities and populations is significant in reducing social vulnerability and disaster risk specifically and effectively. [ABSTRACT FROM AUTHOR]

The synchronicity of rain and heat in the summer of China's monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region. [ABSTRACT FROM AUTHOR]

The frequent occurrence of extreme cold waves under climate change has attracted widespread attention. Based on the Japanese 55-year Reanalysis daily dataset from 1958 to 2021, we use a newly developed dynamic metric, the local finite-amplitude wave activity (LWA), to explore the precursory signals, outburst conditions, and key dynamic features of extreme cold waves over eastern China from the perspective of synoptic climatology. The statistical results show that approximately 40% of extreme cold waves have the following features. First, the formation of significant positive LWA anomalies over the Balkhash–Baikal region is an evident precursory signal, which is accompanied by significant cold surface air temperature anomalies that accumulate over mid- and high-latitude Eurasia. Second, the appearance of extreme positive LWA anomalies over the region east of Lake Baikal (ELB) is necessary for subsequent outbursts of extreme cold waves. These extreme positive LWA anomalies indicate the meridionally enhanced planetary trough over East Asia and advection of the accumulated cold air masses southeastward to eastern China. Third, the evident positive change in the LWA anomalies over the ELB is mainly attributable to the convergence of the zonal LWA flux due to the zonal wind in the eddy-free state and Stokes drift flux over the eastern area of the ELB and the convergence of the meridional eddy heat flux over the western area. This study demonstrates that the LWA could be used as a simple and feasible metric for monitoring and forecasting extreme cold waves. Significance Statement: Enhanced waviness in circulation usually occurs before and during the outburst of extreme cold waves over eastern China. With a state-of-the-art diagnostic tool, the local finite-amplitude wave activity (LWA), the present study reveals both precursory signals and outburst conditions of these extreme cold events from the perspective of synoptic climatology. This study not only deepens our understanding of the dynamic process for extreme cold events over eastern China but also offers a method for monitoring and forecasting those extreme events. Our work also provides a method for studying other extreme climate events that are closely related to large-amplitude circulation waviness over the middle and high latitudes. [ABSTRACT FROM AUTHOR]

It remains a major challenge to attribute heatwave's lifecycle characteristics quantitatively to interwoven atmospheric and surface actions. By constructing a process-resolving, energetics-based attribution framework, here we quantitatively delineate the lifecycle of the record-breaking 2022 mega-heatwave over central-eastern China from a local energetics perspective. It is found that the cloudlessness induced radiative heating and atmospheric dynamics dominate the total energy buildup during the developing stage, while the land-atmosphere coupling and atmospheric horizontal advection act most effectively to sustain and terminate the heatwave, respectively. A reduction in anthropogenic aerosols provides a persistent positive contribution during the event, suggesting that pollution mitigation measures may actually increase the amplitudes of future heatwaves. With this framework, initial efforts are made to unravel culprits in a model's sub-seasonal prediction of this mega-heatwave, demonstrating the framework's potential for efficiently detecting the origins of climate extremes and quantitatively assessing the impacts of mitigation policies for sustainable development. [ABSTRACT FROM AUTHOR]

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China's national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants' growth and extreme climate, together with the reaction of plants' growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants' growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Extensive fires pose catastrophic threats to both human and natural ecosystems. Understanding the history of fire, particularly Holocene palaeofire activity in densely populated areas, is essential for predicting future fire risks and developing effective fire management policies. The complexity of fire activity is influenced by various factors, including climate and anthropogenic activities. In this study, we analysed microcharcoal from the top 35.36 m of a well‐dated sediment core HMD1401 in Ningshao Plain, eastern China. We combined our findings with phytolith and diatom evidence to obtain a comprehensive understanding of variations in Holocene fire activity and its controls. The results showed that there was higher fire activity during the early and late Holocene and less fire activity during the mid‐Holocene. More frequent fire occurred from c. 10 000–7000 cal. a BP and was primarily caused by abundant biomass and high seasonal flammability due to increased annual temperature and precipitation and warm but dry winter climate. Fire occurrences between c. 7000–2000 cal. a BP remained at a low level, except for the periods c. 5900–5600 cal. a BP and c. 5300 cal. a BP, which may have been caused by extreme climate events. The impact of fire caused by human activity was significantly enhanced during the last two millennia. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal variations in climate extremes indices, as well as the influencing factors, is critical to the scientific response to climate change. The temporal and spatial variations of SU25 (annual count of days when daily maximum temperature > 25 °C) were discussed in this study, based on daily maximum temperature data from 2398 meteorological stations in China from 1961 to 2017. The contributions of associated large-scale circulation factors to SU25 were quantitatively assessed by using the geographical detector method (GMD). The overall spatial distribution of SU25 was marked by a considerable increase from north to south. The SU25 increased significantly over time, with the national SU25 increasing at a rate of 2.5 days/decade. The Tibet Plateau (TP) had the slowest growth rate, with an average increase rate of 1.4 days/decade. The Hurst values of SU25 in all the subregions were generally high, indicating that most stations of SU25 would continue to increase in the future. Except for TP, the tipping years of other subregions were concentrated in the 1990s, and SU25 increased after the years. Among the large-scale circulation factors affecting SU25 in each subregion, Atlantic Multidecadal Oscillation (AMO) played a major role in SU25 variability. As a whole, the result of the pairwise interaction of each circulation factor was mainly nonlinear enhancement. The joint contributions of multiple factors to SU25 were larger than the contribution of each individual factor. [ABSTRACT FROM AUTHOR]

Much attention has been given to the change rule of a single extreme event, and there are few reports on comprehensive characteristics of multiple extreme events in a certain region. Based on the analyzes of annual frequency of extreme high temperature, extreme low temperature, extreme drought, extreme precipitation, and extreme typhoon events in China from 1961 to 2020, a multi extreme events composite risk grade index (MXCI) was constructed and applied to the comprehensive characteristics analyzes of multiple extreme events in China. The results show that the high value areas of MXCI were mainly located in southeast China and southwest China. Over the past 60 years, the MXCI has shown a decreasing trend in western China and most of southeastern China, and an increasing trend in the middle zone from southwest China to northeast China. Through comparative analysis, MXCI can objectively reflect the comprehensive characteristics of multiple extreme climate events in a region, which is helpful to understand regional extreme climate characteristics and effectively cope with extreme climate risks. [ABSTRACT FROM AUTHOR]

West China experienced an extremely wet autumn in 2017, and the amount of September–October precipitation is the greatest during 1961–2018. This study explored the physical processes responsible for two primary intraseasonal precipitation events which contribute greatly to such an extremely wet season. The first event peaking in early September is induced by the southward movement of a frontal system from the northern China, which is attributed to the southward displacement of an intraseasonal anticyclonic anomaly. The northerly anomaly associated with the anticyclone advects cold air into West China and thus triggers frontal precipitation. The second event peaking in early October is related to the northward shift and intensification of an intraseasonal anticyclonic anomaly centred over the northwestern Pacific Ocean. The southerly anomaly along the western flank of the anticyclone transports moisture to West China and thus triggers precipitation. The different initiation processes for the two precipitation events may be due to the distinct dynamical and thermal‐dynamical conditions in September and October. West China has sufficient moisture but less dynamical disturbances in September, while it has adequate dynamical disturbances but less moisture in October. [ABSTRACT FROM AUTHOR]

Under the background of global warming, the frequency and intensity of extreme climate have increased, especially extreme high temperatures. In order to correctly predict the changes in the extreme high temperatures in summer in China in this century, it is urgent to deepen the understanding of the characteristics and physical mechanisms of the extreme high temperatures in summer on the centennial timescale. Many researchers have explored the mechanism of the influences of the variability of the solar cycle on climate change, while the mechanism of the influences of the centennial variation of solar activity on climate change remains elusive. Here, we use the outputs from the Control (CTRL) experiment, Total solar irradiance and Orbital (TSI_ORB) experiment, and Orbital (ORB) experiment from Nanjing Normal University-Holocene (NNU-Hol) experiments to study the extreme high temperatures in summer in China during the Holocene. On the basis of verifying the consistency of the centennial period between the TSI (TSI_ORB minus ORB plus CTRL) experiment and the reconstructed data, we compared the centennial variation characteristics of the summer extreme high temperature in the CTRL experiment and the TSI experiment. It shows that under the modulation of total solar irradiance, the centennial spatial pattern of the summer extreme high temperatures changed from dipole mode to uniform mode, with 300-year and 500-year periodicity, compared to the influence of only internal variability. On the centennial time scale, the greatest difference is located in northeast China. The subsidence movement and the reduction of cloud cover caused by the anticyclone under the control of high-pressure lead to the increase of downward solar radiation, thus making a positive center is showed in northeast China on the impacts of total solar irradiance. Furthermore, the center of the Rossby wave train in the barotropic structure of the upper circulation related to the summer extreme high temperature significantly moves northward. This barotropic structure is composed of continuous pressure ridges from Eurasia to North America and the North Atlantic, which is conducive to the increase of the summer extreme high temperatures. Furthermore, we investigated the underlying physical mechanisms. Under the influence of total solar irradiance, the Pacific Decadal Oscillation (PDO) with the same centennial cycle as extreme high temperatures lead to obvious subsidence movement and increase of radiation flux, causing an increase in extreme high temperatures over northeast China. [ABSTRACT FROM AUTHOR]

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO‐2 Model Inter‐comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO‐2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO2 inversions. More importantly, the mean seasonal cycle of the OCO‐2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter‐model consistency at a sub‐regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO‐2 inversions estimate the mean annual NBP in China for 2015–2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO‐2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO‐2 XCO2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime. Plain Language Summary: The magnitude and distribution of China's terrestrial carbon sink remain underconstrained; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals from NASA's Carbon Observatory 2 (OCO‐2) could help reduce this uncertainty. This study revisited China's terrestrial carbon sink estimates based on state‐of‐the‐art OCO‐2 XCO2 inversions, including the Global Carbon Assimilation System OCO‐2 inversion, the Copernicus Atmosphere Monitoring Service OCO‐2 inversion, and those in the OCO‐2 Model Inter‐comparison Project. We found that the assimilation of OCO‐2 XCO2 retrievals offers effective constraints on the spatiotemporal patterns of the terrestrial carbon sink of China. This result suggests that the OCO‐2 XCO2 inversions allow an improved understanding of China's land carbon sink over in situ CO2 inversions and bottom‐up biosphere model simulations, including better representations in spatial distributions and seasonal cycles and more plausible interannual variations. These improvements suggest that the assimilation of OCO‐2 XCO2 retrievals offers effective constraints on the spatiotemporal patterns of the terrestrial carbon sink of China. Key Points: Orbiting Carbon Observatory 2 (OCO‐2) inversions reveal the largest carbon sink in China is in the south on an annual basis, while in the northeast during peak growing seasonThe seasonal cycle appears to be well constrained in the monsoon climate zonesOCO‐2 inversions are able to capture the impacts of climate extremes on China's carbon sink interannual variability [ABSTRACT FROM AUTHOR]

Wetlands and reservoirs are important water flow and storage regulators in a river basin; therefore, they can play a crucial role in mitigating flood and hydrological drought risks. Despite the advancement of river basin theory and modeling, our knowledge is still limited about the extent to which these two regulators could perform such a role, especially under future climate extremes. To improve our understanding, we first coupled wetlands and reservoir operations into a semi-spatially explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. The projection of future floods and hydrological droughts was performed using the hydrological model during different periods (near future: 2026–2050, middle century: 2051–2075, and end century: 2076–2100) under five future climate change scenarios. We found that the risk of future floods and hydrological droughts can vary across different periods – in particular, it will experience relatively large increases and slight decreases. This large river basin will experience flood events of longer duration, with larger peak flows and volume, and of enhanced flashiness compared to the historical period. Simultaneously, the hydrological droughts will be much more frequent, with longer durations and more serious deficits. Therefore, the risk of floods and droughts will, overall, increase further under future climate change even under the combined influence of reservoirs and wetlands. These findings highlight the hydrological regulation function of wetlands and reservoirs and attest that the combining of wetlands with reservoir operation cannot fully eliminate the increasing future flood and drought risks. To improve a river basin's resilience to the risks of future climate change, we argue that the implementation of wetland restoration and the development of accurate forecasting systems for effective reservoir operation are of great importance. Furthermore, this study demonstrated a wetland–reservoir integrated modeling and assessment framework that is conducive to risk assessment of floods and hydrological droughts and that can be used for other river basins in the world. [ABSTRACT FROM AUTHOR]

The roles of rainfall with various intensities in hydraulic erosion processes are obviously different. In-depth knowledge about the spatiotemporal variation in extreme rainfall erosivity is critical for soil erosion risk assessment and formulation of response measures. In the period of 1961–2020, more than 390,000 erosive rainfall data from 212 meteorological stations were collected to explore the erosion characteristics of different intensities of rainfall erosivity across southern China. The asymmetric characteristic of extreme rainfall erosion was analyzed and expressed by an asymmetric change index (ACI) defined in this study. Correlation analysis was applied to study the impact of global extreme climate events on the ACI. The results showed that different patterns of precipitation had evident asymmetric characteristics in rainfall erosivity, and fewer high-intensity precipitation events contributed to much higher total rainfall erosivity, particularly for rainstorms (≥100 mm). The ACI of the rainfall erosivity exhibited evident spatial heterogeneity; insignificant increasing trends were found in the ACIs for heavy rainfall, torrential, and rainstorm erosivities over the past 60 years. The change rate of ACI varied greatly on both monthly and interdecadal scales, and the ACI of rainstorms showed the maximum linear increasing trend on the long-time scale. There were significantly high correlations between the ACI and erosive rainfall days and the ACI and erosive rainfall (p < 0.01), and the correlation coefficients were relatively higher from May to October, where the precipitation was concentrated. Additionally, the El Niño-Southern Oscillation and tropical cyclones clearly influenced the spatiotemporal distribution of the ACI and rainfall erosivity but were limited to specific periods and regions. These results could provide a reference for extreme soil erosion event monitoring and control in southern China. [ABSTRACT FROM AUTHOR]

Identifying the response of runoff changes to extreme climate evolution was of great scientific significance for the rational regulation of watershed water resources and the prevention of hydrological disasters. However, the time–frequency response relationships were not clear. The Yinjiang River watershed, a typical watershed with karst trough valley areas, was chosen to identify the impact of different climatic driving factors on runoff changes from 1984 to 2015. Continuous wavelet transform (CWT), cross-wavelet transform (XWT), and wavelet coherence transform (WTC) were performed to study the response relationship and time–frequency effect between runoff changes and extreme climate change at different time scales. The main results showed that: (1) Twelve extreme climate indices (ECIs) were detected to have a significant impact on runoff changes, mainly on a 6-year time scale; (2) The R10 and Rx1day in extreme precipitation index and SU34.4 and TNx in the extreme temperature index were the main driving factors of runoff changes, which had relatively large impacts on runoff changes in high and low energy vibration regions. However, the remaining eight ECIs that passed the 0.05 confidence level showed relatively large impacts on runoff changes only in low energy vibration regions; (3) The transition of the interaction between ECIs and runoff changes in high and low time–frequency scales was related to the abrupt change characteristics of the ECIs. The correlation of abrupt change was an important reason for the emergence of highly correlated regions that trigger high and low energy vibrations; (4) As a whole, the extreme precipitation events were ahead of runoff changes at the high time–frequency scale and exhibited small lag effects at the low time–frequency scale, while extreme temperature events were mainly ahead of runoff changes. This study has effectively revealed the impact of climate factors at different scales on runoff changes, and provides a theoretical understanding for regulating and managing water resources in karst basins. [ABSTRACT FROM AUTHOR]

The Yarlung Tsangbo Grand Canyon (YGC), one of the world's deepest canyons, is located within the East Himalayas, which are remote and poorly instrumented. In this study, a rain gauge network was established around the YGC region. Data collected from the network for more than three years disclose that the rainfall spatial pattern in the south of the YGC region is relatively homogeneous. Diurnal cycles showed some variations among sites, but a clear floor was visible in the afternoon and peak values were exhibited in the early morning. The monthly precipitation in the YGC region shows two peaks, in April and July. There are two regions (500 m and 2,500 m AMSL) with high precipitation in the YGC. For rainfall extremes, vertical convection and vapor transport are important in this region. The diurnal cycle from our observations provides a key test of the physical parameterizations for use in the fifth‐generation ECMWF (European Centre for Medium‐Range Weather Forecasts) reanalysis product (ERA5). Our observations indicate that ERA5 cannot reproduce the diurnal patterns of precipitation in the YGC region. ERA5 showed a wet bias when estimating light cumulus rainfall and a dry bias when estimating heavier (convective) precipitation. The erroneous diurnal variation of ERA5 precipitation (false afternoon rainfall) was due to the CAPE (Convectively Available Potential Energy)‐based convective precipitation scheme. The higher ERA5 precipitation than observed was due to the large‐scale rainfall scheme in the Integrated Forecasting System (IFS) of ERA5. These findings indicate that there still is room for improvement of the ERA5 reanalysis data in the eastern Himalaya region. [ABSTRACT FROM AUTHOR]

The increased frequency of climate extremes associated with ongoing climate change has the potential for significant impacts on maize grain yield in maize-producing regions worldwide. In this study, we used a modeling approach to investigate the effects of different types of climatic extremes on maize grain yield in Northeast China. We combined annual mean air temperature and accumulated precipitation data and divided the climate years into 9 categories, using the 10th and 90th percentiles as extreme thresholds. The climate data were represented by 1 normal climate type, 4 individual climate extremes, and 4 combined climate extremes. Changes in the frequencies of climate extremes and their impacts on maize grain yields were analyzed for two 30-year periods (period I: 1960–1989; period II: 1990–2019) using the Agricultural Production Systems sIMulator (APSIM-Maize). Compared with period I, the occurrences of the normal climate and all 3 cold-related climate extremes (i.e., cold-normal, cold-humid, and cold-dry) decreased during period II, while occurrences of the other climate extremes increased. Moreover, according to the APSIM-Maize model, the estimated yield in the years with a normal climate decreased by 4.01%; the 3 cold-related climate extremes increased yields by 26.56%, 12.10%, and 6.68%, respectively; the 2 warm-related climate extremes (warm-normal and warm-dry) and normal-humid years decreased estimated yields by 6.39%, 24.12%, and 5.10%, respectively. The increase in the occurrence of warm-humid years increased the estimated yield by 11.69%. This work confirms the adverse effects of warmer temperatures in the absence of excess rainfall on grain yield and highlights the importance of extremely dry or wet conditions under normal temperature conditions. [ABSTRACT FROM AUTHOR]

The September rainfall over Northern China (NC) in 2021 was the heaviest since 1961 and had unprecedented socioeconomic impacts. Holding the hypothesis that the drivers of extreme climate events usually contain extreme factors, we firstly propose the Ranking Attribution Method (RAM) to find the possible air–sea multi-factors responsible for this rainfall event. Via the atmospheric bridges of zonal-vertical circulation and Rossby wave energy propagation, the remote factors of warm sea surface temperature anomalies (SSTA) over the tropical Atlantic, cold SSTA over the tropical Pacific, Southern Annular Mode-like pattern in the Southern Hemisphere and North Pacific Oscillation-like pattern in the Northern Hemisphere jointly strengthened the Maritime Continent (MC) convection and Indian monsoon (IM). Through meridional-vertical circulation, the intensified MC convection enhanced the subtropical high over southern China and induced ascending motion over NC. The local factor of extreme air acceleration in the east Asian upper-level jet entrance region further anchored the location of the southwest-northeast rain belt. The strengthened IM and subtropical high over southern China induced considerable moisture transport to the rain belt via two moisture channels. The combined effect of these extreme dynamic and moisture conditions formed this unprecedented rainfall event. This study suggests that the RAM can effectively reveal the factors that contributed to this extreme rainfall event, which could provide a new pathway for a better understanding of extreme climate events. [ABSTRACT FROM AUTHOR]

The decadal variability of the summer extreme precipitation over southern China (EPSC) is remarkable, especially for the significant decadal enhancement after the 1990s. The study documented that the summer sea surface temperature (SST) over the North Atlantic and spring sea ice concentration (SIC) over the East Siberian Sea can significantly affect the EPSC. The summer SST over the North Atlantic influences the low-pressure cyclone in the western Pacific by modulating the SST over the tropical Pacific, thus affecting EPSC. A decrease in the SIC of the East Siberian Sea induces a negative Arctic Oscillation, which induces the increased SST over northwest Pacific and the anomalous cyclone over there, in turn, affecting EPSC. Both predictors have a quasi-period of 10–14 years, which provides useful predictive signals for EPSC. The leading 7-year SST and the leading 5-year SIC are chosen to establish the prediction model based on the decadal increment method, which can well predict the EPSC, especially for the shift in the early 1990s. These results provide a clue to the limited predictability of decadal-scale extreme climate events. [ABSTRACT FROM AUTHOR]

Understanding the impact of climate change on runoff and its extremes is of great significance for water resource assessment and adaptation strategies, especially in water-scarce regions. This study aims to analyze the impact of future climate change on runoff and its extremes in the upper reaches of the Heihe River basin in northwest China. The projected runoff was derived using the Soil Water Assessment Tool with climate data from the CSIRO-MK-3-6-0 model under the scenario of RCP4.5, and a frequency analysis of runoff was performed by generalized extreme value distribution. The results indicate that, compared with the baseline period of 1961 to 2000, the minimum and maximum temperatures in the period 2031 to 2070 were predicted to increase by 2.5 °C on average. The precipitation in most months was also predicted to increase, with an average rise of 16.5%. The multi-year average runoff was projected to increase by 8%. The annual mean and extreme flows were also expected to rise under future climate change at different return periods, and the low flow was expected to increase the most. [ABSTRACT FROM AUTHOR]

Potential vegetation distribution is an important study in environmental sciences. We utilized the Mixed Least Squares–Total Least Squares (MLS-TLS) method and the Signal Mode Decomposition method and the Ecological Niche model to identify the inter-correlations of internal climate change factors and constructed an environmental factor response regression model. We identified the resonance periods and trend relationships among climate factors (temperature, precipitation, and evapotranspiration) and found that the evapotranspiration of the watershed interferes with the correlation between temperature and precipitation on a five-year scale. The specific change degree of extreme climate indicators in the region was quantified by the Range of Variability Approach, among which the precipitation indicators were all below 33% (low change). There were significant differences between the key bioclimatic variables and Aspect of the development of suitable vegetation habitats. The difference between the Aspect and average daily air temperature is the main contributor to the spatial distribution of vegetation, and the mutual contribution is 76.19%. Our regression model can effectively simulate the potential distribution of vegetation (r = 0.854). Compared to the MaxEnt model, our regression model can quantitatively and intuitively provide suitable habitat values for Cryptomeria fortunei at any given location in the basin. Under future scenarios (2021–2040), suitable habitat for Cryptomeria fortunei in the eastern and western regions of the basin is projected to deteriorate further. The research results can provide some help for policymakers to eliminate the potential adverse effects of future climate change on regional ecology. [ABSTRACT FROM AUTHOR]

The extent and mechanisms by which precipitation extremes affect hydrological processes in the critical zone (CZ) of loess possessing a thick unsaturated zone remains poorly understood. To this end, we here employed a coupled liquid‒vapor‒heat‒airflow STEMMUS (simultaneous transfer of energy, mass, and momentum in unsaturated soil) model to investigate the impact of extreme precipitation, at both event and annual scales, on CZ hydrological processes within a semiarid loess site vegetated by apple trees on China's Loess Plateau. At the event scale, the vapor flux was two orders of magnitude lower than the liquid water flux. However, the thermal vapor flux penetrated to depths of 200 cm, whereas the isothermal liquid water flux only infiltrated to 100 cm during the study period, implying that thermal‐gradient‐driven vapor transfer is an important mechanism for deep‐layer recharge (DLR). At the annual scale, the DLR below 200 cm during extremely wet years was 6.5 times larger than that during extremely dry years. In contrast, extreme changes in climate had only limited impacts on evapotranspiration; the difference between extremely wet and extremely dry years averaged only 35 mm, much less than the difference in precipitation, which averaged 310 mm. However, the extremely dry years showed a higher ratio (0.58) between transpiration and evapotranspiration than did the extremely wet years (0.51). The findings reported here improve our understanding of CZ hydrological processes related to precipitation extremes in semiarid loess regions. Key Points: Thermal‐gradient‐driven vapor transfer is an important mechanism for deep‐layer recharge (DLR) in loessThe DLR below 200 cm during extremely wet years was 6.5 times larger than that during extremely dry yearsExtreme changes in precipitation had only limited impacts on evapotranspiration [ABSTRACT FROM AUTHOR]

Non-methane short-lived climate forcers (SLCFs), including aerosols, ozone, and their precursors, are important climate forcings and primary air pollutants. Future stringent SLCF emissions controls to mitigate air pollution will substantially impact regional climate change. Here, we investigate the changes in future climate extremes and resulting population exposure risks in Asia during 2031–2050 in response to non-methane SLCF emissions reductions using multi-model ensemble (MME) simulations under two shared socioeconomic pathway (SSP) scenarios with the same greenhouse gas (GHG) emissions but weak (SSP3-7.0) and strong (SSP3-7.0-lowNTCF) air quality control measures from the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP), which is endorsed by the Coupled Model Intercomparison Project phase 6 (CMIP6). The MME results show that future reductions in non-methane SLCF emissions lead to an increase of 0.23 ± 0.16 W m -2 in global annual mean effective radiative forcing, thereby magnifying the GHG-induced global surface warming by 0.19 ± 0.1 K during 2031–2050. In terms of the entire study area of Asia, the additional warming caused by the non-methane SLCF reductions increases the temperature of the hottest days (TXx) by 0.3 ± 0.1 K, the percentage of warm days (TX90p) by 4.8 ± 2.2 %, the number of tropical nights (TR) by 1.7 ± 0.8 d, the warm-spell duration (WSDI) by 1.0 ± 0.4 d, the number of heavy precipitation days (R10) by 1.0 ± 0.5 d, the maximum consecutive 5 d precipitation (RX5day) by 1.0 ± 0.3 mm, and the total wet-day precipitation (R95p) by 16.4 ± 7.3 mm during 2031–2050. In terms of sub-regional changes in temperature extremes, the largest regional increases in TXx, TX90p, and WSDI occur in northern India (NIN) and northern China (NC). Relatively large increases in TR are projected in NC and the Sichuan Basin (SCB), reaching 5.1 ± 2.5 d and 4.9 ± 3.3 d, respectively. For precipitation extremes, the regional changes are greatest in southern China (SC), particularly southwestern China (SWC), where reductions in non-methane SLCF emissions increase R10 by 2.5 ± 1.9 d, RX5day by 2.5 ± 1.5 mm, and R95p by 37.5 ± 22.6 mm. Moreover, the populations exposed to temperature and precipitation extremes increase most sharply in NIN, reaching (32.2 ± 11.4) × 10 7 person-days and (4.6 ± 6.1) × 10 6 person-days during 2031–2050, respectively, followed by NC and SCB. Our results highlight the significant impacts of non-methane SLCF reductions on future climate extremes and related exposure risks in eastern and southern Asia, which are comparable to the impact associated with increased GHG forcing in some regions. [ABSTRACT FROM AUTHOR]