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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]

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]

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]

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]

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]

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]

The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments. This paper reviews the evidence of the anthropogenic influence on regional climate over China from the perspectives of surface air temperature (SAT), precipitation, droughts, and surface wind speed, based on studies published since 2018. The reviewed evidence indicates that human activities, including greenhouse gas and anthropogenic aerosol emissions, land use and cover change, urbanization, and anthropogenic heat release, have contributed to changes in the SAT trend and the likelihood of regional record-breaking extreme high/low temperature events over China. The anthropogenically forced SAT signal can be detected back to the 1870s in the southeastern Tibetan Plateau region. Although the anthropogenic signal of summer precipitation over China is detectable and anthropogenic forcing has contributed to an increased likelihood of regional record-breaking heavy/low precipitation events, the anthropogenic precipitation signal over China is relatively obscure. Moreover, human activities have also contributed to a decline in surface wind speed, weakening of monsoon precipitation, and an increase in the frequency of droughts and compound extreme climate/weather events over China in recent decades. This review can serve as a reference both for further understanding the causes of regional climate changes over China and for sound decision-making on regional climate mitigation and adaptation. Additionally, a few key or challenging scientific issues associated with the human influence on regional climate changes are discussed in the context of future research. [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]

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]

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]

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]

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]

Quantifying the urbanization effect on trends in climate extremes is important both for detection and attribution studies and for human adaptation; however, a fundamental problem is how to accurately estimate a trend and its statistical significance, especially for non-Gaussian and serially dependent data. In this paper, the choice of trend estimation and significance testing method is suggested as important for these kinds of studies, as illustrated by quantifying the urbanization effect on trends in seven hot-extreme indices for the megacity of Shanghai during 1961-2013. Both linear and nonlinear trend estimation methods were used. The trends and corresponding statistical significances were estimated by taking into account potential non-Gaussian and serial dependence in the extreme indices. A new method based on adaptive surrogate data is proposed to test the statistical significance of the ensemble empirical mode decomposition (EEMD) nonlinear trend. The urbanization contribution was found to be approximately 34 % (43 %) for the trend in the non-Gaussian distributed heat wave index based on nonparametric linear trend (EEMD nonlinear trend) estimation. For some of the other six hot-extreme indices analyzed, the urbanization contributions estimated based on linear and nonlinear trends varied greatly, with as much as a twofold difference between them. For the linear trend estimation itself, the ordinary least squares fit can give a substantially biased estimation of the urbanization contribution for some of the non-Gaussian extreme indices. [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]

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]

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 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]

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]

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]

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models. Plain Language Summary: Given the serious hazards of extreme temperature events, understanding changes in extreme temperatures and effectively predicting their future changes are critical to climate mitigation and adaptation. According to station observations and model data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6), we find besides the varying temperature mean, the varying temperature variance also has an important impact on changes in extreme temperatures, and CMIP6 models have overall better performance than CMIP5 models in reproducing changes in extreme temperatures in China. Our results also emphasize that good performances in historical simulation cannot guarantee better projections in the future and projections of extreme temperatures from the multimodel ensemble sacrifice the spatial heterogeneity relative to individual reference models. Key Points: The impact of the varying natural variability on changes in temperature extremes depends on the variability indexCMIP6 models perform better than CMIP5 in simulating temperature extremes changes in ChinaMultimodel ensemble projections of extreme temperatures sacrifice spatial heterogeneity [ABSTRACT FROM AUTHOR]

Bamboo is a plant that may replace plastics and wood as a new green environmental protection material that can be developed. Ferrocalamus strictus is a very special and rare bamboo which is a plant species with extremely small populations (PSESP) endemic to Yunnan, China, and it is listed as a China key national first-class protected wild plant. To explore the effects of extreme climate and environmental changes on this PSESP, we conducted a case study on the response of F. strictus to climate under two extreme future emission scenarios. We combined 29 environmental variables, including the temperature, precipitation, altitude, slope, aspect, soil pH, soil organic carbon content, and mean UV-B radiation of the highest month, to analyze the important environmental factors limiting the distribution of F. strictus and identified the priority conservation areas (PCAs) of F. strictus. Under the two future extreme climate scenarios, the core potential suitable distribution area of F. strictus would decrease by 26%–34% in 2040, 44%–46% in 2060, and 23%–58% in 2080, respectively. Bio18 (precipitation in the warmest quarter) and Bio3 (isotherm) were the two key factors limiting the geographical distribution of F. strictus (the contribution rates were 40.1% and 18.45%, respectively). The results of this study show that F. strictus is in urgent need of protection and PSESPs require more attention. [ABSTRACT FROM AUTHOR]

A compound perspective on hydrological extreme events is of paramount significance as it may lead to damages with larger losses. In this study, an integrated framework, based on downscaled climate variables and hydrological model, i.e. the Soil and Water Assessment Tool, was applied to generate extreme precipitation (Rx1day) and extreme streamflow (Sx1day) series under historical and future climate conditions. Then the potential impacts of climate change for univariate and bivariate joint frequency of extreme precipitation and flood in Xitiaoxi River Basin (XRB), a representative watershed of the Yangtze River Delta, are detected. The compound risk of extreme precipitation and flood under different levels of joint return period for historical and projected periods is estimated by copula‐based two-dimensional approaches. The Rx1day and Sx1day under future scenarios changed by − 0.4% to 11.7% and 0.7% to 20.4%, respectively, compared to historical period based on univariate frequency analysis, indicating the increasing magnitude of the flood in the future. Climate change with different emission scenarios all have a driving effect on the rising coactivity of extreme precipitation and flood under compound flooding frequency analysis. In addition, the enhancement of climate change to extreme events is more apparent for extremes with higher return period and under the periods of 2080s. Moreover, the flood frequency designs are deduced by bivariate joint distribution are safer than that by univariate distribution. This study may provide actionable insights to formulate the planning scheme of flood control and disaster reduction under the changing environment. [ABSTRACT FROM AUTHOR]

The evident climate jump after 2000 in China may have greatly influenced the production of winter wheat, which is one of the nation's major grain crops. To evaluate the impacts of climate change on winter wheat production and identify the climatic factors primarily responsible, we used daily meteorological data from 2244 stations and integrated indicators to examine the decadal changes in the potential plantable zone (PPZ), growth periods, and climatic suitability for winter wheat in China from 1985 to 1999 and from 2000 to 2014. The results showed the following: (1) The PPZ has decreased by approximately 9%, and the main reason may be the increased frequency of extreme cold events in northern China from 2000 to 2014. (2) In most of the PPZ, the suitable sowing date has been delayed, the potential maturity date has advanced, and total days during the potential growing season have significantly decreased because of the increasing temperature. (3) The suitable area and optimal area of winter wheat have significantly decreased by 9% and 13%, respectively. The changes in climatic suitability are affected by both temperature and radiation in the north, whereas the impact is more from precipitation in the south. The climate may be changing in a direction unsuitable for winter wheat. As global warming and climate extremes intensify in the future, winter wheat production may become more challenging, and adequate measures should be adopted to guarantee reliable and high yields. [ABSTRACT FROM AUTHOR]

The Changbaishan volcano, located in northeast China, produced a large amount of charcoal during the "Millennium Eruption" (c. 946 CE). In this paper, we report a high-resolution date for the eruption using multiple methods applied to the fossil charcoal and living trees, including wood anatomy, radiocarbon dating and tree-ring cross-dating. In addition, we develop a new floating annual-resolution tree-ring chronology spanning nearly 200 years that provides information about paleoclimate conditions. In our study, we identified 60 samples of charcoal as Larix olgensis based on anatomical characteristics and geographical location. Using these materials, we dated the eruption to the winter of 946 CE based on a combination of radiocarbon dating, traditional dendrochronological cross-dating and cambial cell growth status. This allowed us to develop an L. olgensis tree-ring width chronology covering 764–946 CE and 1715–2016 CE, with significant sensitivities to April–September temperature (p < 0.05). The tree-ring data have great potential to indicate extreme climate events; for example, we identify an extremely cold year in 903 CE. Such marker years can improve the reliability and accuracy of tree-ring data. Our study provides direct and precise tree-ring evidence for volcanic eruptions and anomalous climate years in the Middle Ages. [Display omitted] • Cambial activity enables us to date the Millennium Eruption to winter 946 CE. • The first chronology reaching the Middle Ages so far developed in northeastern China. • The chronology was assembled from volcanic charcoal and living larch. • This chronology is sensitive to temperature. [ABSTRACT FROM AUTHOR]

Global climate change affects all aspects of human society, especially agricultural and animal husbandry production. Northwest China has been detrimentally affected by the climatic variations due to its high exposure to extreme climatic events. A number of studies have reported agro-pastoralists' perceptions and adaptation responses to climate change, but the current knowledge of agro-pastoralists' perceptions of climate change in China are insufficient. To fill this research gap, this study aims to investigate the perception level of agro-pastoralists in Northwest China on climate change and related factors. Data were collected using a structured questionnaire based on household surveys of 554 study participants in four counties in Gansu Province, China. Raw data were collected using stratified random sampling. A probit model was used to analyze the respondents' understanding of climate change and its related socio-economic and demographic variables. Our results show that the majority of respondents were aware (70%) of the changes in temperature and precipitation. Socioeconomic and demographic variables such as gender, farming experience, education level, cultivated land size, agricultural income, livestock, village cadre experience, access to weather information of agro-pastoralists are pertinently related to agro-pastoralists' awareness of climate change. Farming experience, education level, household size, grassland size, agricultural income, association membership, village cadre experience has a high impact on agro-pastoralists' adaptation to climate change. The results of this study will help guide government agencies and decision makers, and help arid and semi-arid areas to build sustainable adaptation measures under the framework of climate change. The study recommends institutions targeting households' livelihood improvement and making decisions concerning climate change adaptation need to focus on mass media and information technology, improving locally adapted extension services, improved irrigation, expand loan channels. [ABSTRACT FROM AUTHOR]

Of all forest biomes, boreal forests are experiencing the most significant warming. Drought caused by warming has a dramatic impact on species in boreal forests. However, little is known about whether the growth of trees and shrubs in boreal forests responds consistently to warming and drought. We obtained the tree-ring width data of 308 trees (Larix gmelinii and Pinus sylvestris var. mongolica) and 133 shrubs (Pinus pumila) from 26 sites in northeastern China. According to the climate data from 1950 to 2014, we determined three extreme drought years (1954, 1967, and 2008). The response difference of radial growth of trees and shrubs in boreal forests to drought was compared using resilience index, moving correlation and response analysis. The results showed that high temperature (mean and maximum temperature) in previous and current growing seasons promoted the growth of P. pumila , but inhibited the growth of trees. On the contrary, wetter conditions (higher PDSI) promoted tree growth but were not conducive to P. pumila growth in high latitudes. Moving correlation analysis showed similar results. In addition, water deficit was more likely to inhibit P. pumila growth in low latitudes. The drought resistance of P. pumila was stronger than that of L. gmelinii and P. sylvestris var. mongolica. Therefore, the growth loss and recovery time of P. pumila during drought was less than those of trees. We concluded that L. gmelinii and P. sylvestris var. mongolica are more prone to growth decline than P. pumila after the drought caused by climate warming. In the future climate warming, shrub growth may benefit more than trees. Our findings are of great significance in predicting the future changes in ecosystem composition and species distribution dynamics in extreme climate susceptible areas. [ABSTRACT FROM AUTHOR]

The South-to-North Water Diversion Project (SNWDP) is a national strategic project for water shortages in northern China. Climate change will affect the availability of water resources in both source and receiving areas. A grid-based RCCC-WBM model based on climate projections from nine Global Climate Models under SSP2-4.5 was used for analyzing the changes in temperature, precipitation, and streamflow in the near future (2025–2045, NF) and far future (2040–2060, FF) relative to the baseline (1956–2000). The results showed that: (1) the temperature of the western route will increase significantly in the NF and FF with an extent of 1.6 °C and 2.0 °C, respectively, (2) precipitation will very likely increase even though Global Climate Model (GCM) projections are quite dispersed and uncertain, and (3) over half of the GCMs projected that streamflow of receiving area will slightly increase with a rate of 1.68% [−8.67%, 12.3%] and 2.78% [−3.30%, 11.0%] in the NF and FF, respectively. Climate change will support the planning of the western route to a certain extent. However, water supply risk induced by the extreme situation of climate change should be paid adequate consideration when the project operates in practice due to the large dispersion and uncertainty of GCM projections. [ABSTRACT FROM AUTHOR]

Understanding the evolution of heatwaves is important for their prediction, mitigation, and adaptation. While most studies focused on either their temporal variability at individual station (or grid point) or the spatial variation over fixed durations, their daily joint evolution over space and time remains largely unexplored. Here, we investigate multi‐dimensional characteristics of spatiotemporally contiguous heatwaves across China during 1961–2018 using observational data from a 3D perspective (i.e., latitude × longitude × time). Results obtained by the 3D identification show that large contiguous heatwaves in China exhibit distinct characteristics in different geographical locations and time periods. Heatwaves in northern areas prefer to move from west to east at a faster speed, persist longer, and have greater extent and intensity than southern heatwaves, which are generally originated from the east and move westward. It is also found that contiguous heatwaves are growing in frequency, magnitude, areal extent, duration, and traveling distance across China. Plain Language Summary: Known as episodes with temperature exceeding a certain threshold, heatwaves exert severe detrimental impacts on human health. Understanding their joint evolution at both temporal and spatial dimensions is beneficial for us to mitigate the adverse effects and adapt ourselves to future heatwave increases under global warming. However, previous studies mainly focused on either the temporal or spatial changes of heatwaves rather than their co‐variability over both time and space. Here we introduce a new method, the 3D connected component algorithm, to track the 3D evolution of spatiotemporally contiguous heatwaves over China during 1961–2018 using observational data. We find that contiguous heatwaves over northern China prefer to move eastward at a faster speed, persist longer, and have greater extent and intensity than southern heatwaves, which are generally originated from the east and move westward. Contiguous heatwaves show significant increases in frequency, magnitude, coverage, duration, and traveling distance. Key Points: True 3D structure and spatiotemporal evolution of large contiguous heatwave over China are tracked by the 3D connected component labeling algorithmContiguous heatwaves in China exhibit distinct characteristics and evolution patterns in different geographical locations and time periodsSubstantial increases are observed in the frequency, area, magnitude, duration, and moving distance of contiguous heatwaves since the 1960s [ABSTRACT FROM AUTHOR]

In this study, the temporal and spatial characteristics of extreme climate from 1961 to 2017 were analyzed using daily precipitation and temperature data from 24 meteorological stations in the Qilian Mountain area, northwestern China. The results showed that warming accelerated considerably after 1985, particularly during the 1990s. The warming trend then increased after 2000 and further increased after 2010. The Qilian Mountains and the Hexi and Qaidam Inland River Basins greatly affected the Atlantic Multidecadal Oscillation (AMO), Northern Tropical Atlantic Index (NTA), Caribbean sea-level surface temperature (SST) Index (CAR), South China Sea Summer Monsoon Index (SCSSMI), and South American Summer Monsoon Index (SAMSMI). The circulation index on the extreme temperature warm index was stronger than that of the extreme temperature cold index. There were greater increases in all extreme indexes in the central part of the Qilian Mountain area, and the region of increase decreased from inside to outside. The interannual change in the warming index of extreme temperature was similar to that of the cold index of extreme temperature. TX10, TN10, ice days (ID), and frost days (FD) showed significant negative correlations with altitude, whereas TXN and TNN showed significant positive correlations with altitude. Changes in TX10, TN10, TXN, TNN, ID, FD, and diurnal temperature range were most noticeable in high-altitude areas (> 2500 m), whereas changes in TN90, TX90, TXX, TNX, and growing season length were most noticeable in low altitude areas (< 2500 m). The Qilian Mountain area and the Hexi and Qaidam Inland River Basins were greatly affected by the AMO, NTA, CAR, SCSSMI, and SAMSMI; however, they were only slightly affected by the Nino4, Northern Atlantic Oscillation, North Pacific model, Southern Oscillation Index, Arctic Oscillation, and Multiple ENSO Index. The effect of the circulation index of Atlantic Multidecadal Oscillation, Tropical Northern Atlantic Index, Tropical Southern Atlantic Index, North Tropical Atlantic SST Index, and CAR on the extreme temperature warm index was stronger than that of the extreme temperature cold index. [ABSTRACT FROM AUTHOR]

China's climate has been warming since the 1950s, with surface air temperature increasing at a rate higher than the global average. Changes in climate have exerted substantial impacts on water resources, agriculture, ecosystems and human health. Attributing past changes to causes provides a scientific foundation for national and international climate policies. Here, we review recent progress in attributing the observed climate changes over past decades in China. Anthropogenic forcings, dominated by greenhouse gas emissions, are the main drivers for observed increases in mean and extreme temperatures. Evidence of the effect of anthropogenic forcings on precipitation is emerging. Human influence has increased the probability of extreme heat events, and has likely changed the occurrence probabilities for some heavy precipitation events. The way a specific attribution question is posed and the conditions under which the question is addressed present persistent challenges for appropriately communicating attribution results to non-specialists. [ABSTRACT FROM AUTHOR]

Extreme climatic events such as drought and high temperatures are frequently occurring under the global climate change scenarios causing severe threats to crop yield. Several studies have used individual events, e.g., meteorological drought (dry event), high temperatures (hot event), or compound dry–hot events as predictors of agricultural drought severity. However, only a few studies considered the situation when at least one dry event or hot event appeared. Moreover, the ability to depict drought phenomena, in terms of duration and severity, with the same drought index for different time scales was often inconsistent. This study adopted the meta-Gaussian model (MG) to predict agricultural drought severity for the crops and vegetation growing seasons (from April to September) from the year 1948 to 2014 in China by using two predictors, namely, standardized dry or hot events index (SDHEI) and joint soil moisture deficit index (JSDI). The SDHEI was built based on monthly precipitation and daily temperature, and the JSDI was calculated by combining 1-, 3-, 6-, 9-, and 12-month standardized soil moisture index (SSI) based on monthly root zone soil moisture. The prediction performance of the MG was assessed by employing the Brier skill score (BSS), Nash–Sutcliffe efficiency coefficient (NSE), and root mean square error (RMSE). The severe agricultural drought in the months of April–September of 2010 in China was used as a case study. A visual comparison of this event indicated that the lead JSDI predictions for 1–3 months showed a resembled behavioral pattern with corresponding historical observations for most of the areas of China; however, it had some disagreements in the spatial extent and severity of the agricultural drought in some of the regions. The MG had a great performance with BSS > 0.4, NSE > 0.6, and RMSE < 0.6 for the 1-month lead prediction of JSDI in most of the study areas; however, it turned to be weaker in some regions for the 3-month lead prediction. Overall, the prediction performance of MG in April–June outperformed the same in July–September under the same lead prediction of JSDI. The outcomes of this study might provide scientific guidance for agricultural drought monitoring, early warning, and drought decision-making in China or other countries. [ABSTRACT FROM AUTHOR]

Technological advances have the potential to balance climate change mitigation and economic development. However, it remains unclear how much technological advances alone can mitigate climate change and the associated economic losses in the future. Through designing a suite of technological advances scenarios and using an earth system model with an integrated assessment model, we illustrate that rapid technological progress without production control might achieve the 2°C global warming target in the 2100s. Relative to a world of stagnant technology, the frequency (intensity) of extreme warm events at the end of the 21st century (2081–2100) would be reduced by ∼21% (5.5°C) via rapid technological advances, with a reduction in extreme precipitation (droughts) by ∼41% (10 times). Furthermore, fast technological advances may reduce the global economic losses linked with climate change at 2081–2100 by ∼21% and those in China related to floods (droughts) by 86% (67%). Our results highlight the potential of technological advances to fill the emission gap between the Paris Agreement and unconditional Nationally Determined Contributions and hence to efficiently mitigate global warming. [ABSTRACT FROM AUTHOR]

Drought is a major natural disaster that significantly impacts the susceptibility and flexibility of the ecosystem by changing vegetation phenology and productivity. This study aimed to investigate the impact of extreme climatic variation on vegetation phenology and productivity over the four sub-regions of China from 2000 to 2017. Daily rain gauge precipitation and air temperature datasets were used to estimate the trends, and to compute the standardized precipitation-evapotranspiration index (SPEI). Remote sensing–based Enhanced Vegetation Index (EVI) data from a moderate resolution imaging spectroradiometer (MODIS) was used to characterize vegetation phenology. The results revealed that (1) air temperature had significant increasing trends (P < 0.05) in all sub-regions. Precipitation showed a non-significant increasing trend in Northwest China (NWC) and insignificant decreasing trends in North China (NC), Qinghai Tibet area (QTA), and South China (SC). (2) Integrated enhanced vegetation index (iEVI) and SPEI variations depicted that 2011 and 2016 were the extremely driest and wettest years during 2000–2017. (3) Rapid changes were observed in the vegetation phenology and productivity between 2011 and 2016. In 2011, changes in the vegetation phenology with the length of the growing season (ΔLGS) = was − 14 ± 36 days. In 2016, the overall net effect changed at the onset and end of the growing season with ΔLGS of 34 ± 71 days. The change in iEVI per SPEI increased rapidly with a changing rate of 0.16 from arid (NWC, and QTA) to semi-arid (NWC, QTA and NC) and declined with a rate of − 0.04 from semi-humid (QTA, NC, and SC) to humid (SC) region. A higher association was observed between iEVI and SPEI as compared to iEVI and precipitation. Our finding exposed that north China is more sensitive to climatic variation. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the projected regional responses of univariate and bivariate behaviors of extreme precipitation to climate change over the upper-middle Huaihe River Basin. Based on twelve GCM outputs under historical, RCP4.5 and the observations at 32 rainfall stations, the equidistant cumulative distribution function matching method (EDCDFm) was utilized to bias correct daily precipitation during the historical (1961–2005) and future (2021–2080) periods. Four precipitation indices combinations were introduced based on eight precipitation indices to characterize the regional-scale changes of precipitation events, which designate the duration, intensity and amount of heavy and weak precipitation in a year. Their dependence structures were captured by Copulas. Kendall return period (KRP) were applied to discuss hazard scenarios and we quantified the spatial variability of KRPs under different marginal values. The results indicated that projected precipitation characteristics including the average intensity, the amount of annual precipitation, the intensity and amount of extreme precipitation together with annual extremes displayed increasing trends, while the changes of consecutive wet and dry days did not present pronounced trends. Decreased KRPs in the vast majority of the territory manifested that the frequency of simultaneous floods and droughts in a year as well as that of extreme heavy precipitation events would augment. Obvious spatial heterogeneity of the changes of KRP was partly attributed to the topography difference, especially the coastal areas along the main stream of the Huaihe River. Consequently, there will be a higher risk of water resources-related issues in this region for upcoming decades. • Significant decreases will occur in the intensity and amount of heavy precipitation for upcoming decades. • Change rates of precipitation indices combinations illustrate obvious spatial heterogeneity. • The frequency of simultaneous floods and droughts in a year and that of extreme heavy precipitation events will augment. [ABSTRACT FROM AUTHOR]

Climate warming and subsequent climate extremes have a wide impact on ecosystems and economic society. Scientifically quantitative assessment on climate change risks is an urgent issue attracted the attention of academics and decision-makers. In this paper, the theory of climate change risks was sorted out based on the three component elements of hazard, vulnerability, and exposure. Moreover, the idiomatic assessment methods and their applicability were systematically summarized. Comprehensive analysis found that the impacts of climate change generally showed a negative trend that was more harm than benefit, and further warming would aggravate the risk over China which was manifested to be more prominent in some sensitive sectors and key regions to climate change. Accessible management of the climate change risk should be engaged in effectively improving the methods of comprehensive identification, strengthening the mechanism and quantitative assessment, recognizing the temporal and spatial variation of components related to climate change risk, and reducing the uncertainty of climate change projection. • Theoretical framework of climate change risk was formed based on hazard, vulnerability, and exposure. • Assessment methods of climate change risk and their applicability were systematically summarized. • Climate change risk levels were analyzed in sensitive sectors and key regions over China. • In China, impacts of climate change were more harm than benefit while more warming was very likely to aggravate the risk. [ABSTRACT FROM AUTHOR]

Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin. [ABSTRACT FROM AUTHOR]

Private diaries are important sources of historical data for research on climate change. Their advantages include a high veracity and reliability, accurate time and location information, a high temporal resolution, seasonal integrity, and rich content. In particular, these data are suitable for reconstructing short-term, high-resolution climate series and extreme climatic events. Through a case study of Yunshan Diary , authored by Bi Guo of the Yuan dynasty of China, this article demonstrates how to delve into climate information in diaries, including species distribution records, phenological records, daily weather descriptions and personal experiences of meteorological conditions. In addition, this article considers how to use these records, supplemented by other data, to reconstruct climate change and extreme climatic events on various timescales, from multidecadal to annual or daily. The study of Yunshan Diary finds that there was a relatively low amount of precipitation in central and southern Jiangsu Province in the summer of 1309; the winter of 1308–1309 was abnormally cold in the Taihu Lake basin. In the early 14th century at the latest, the climate in eastern China had begun to turn cold, which reflects the transition from the Medieval Warm Period to the Little Ice Age. [ABSTRACT FROM AUTHOR]

The last few years have seen a surge of scholarly interest in how cultures have been influenced by climate, climatic changes, and extremes of weather. This "cultural turn" of climate history draws from the archives of society, rather than the archives of nature, and is heavily influenced by interpretative and methodological frameworks drawn from the humanities fields. Attention has been largely focused on European contexts and cultures. This article aims to show, however, that the climate history of Asia is on the cusp of developing its own strong cultural turn and that strong foundations and precedents have already been set. This article has two main objectives. The first is to explore approaches in the history of the climate in East Asia that claim to investigate interactions between climatic changes or extreme events and society and culture. Many of these are macro studies, comparing climatic changes with dramatic events in history. Then, it will take a closer look at scholarship that has focused closely on the ideas and beliefs that characterize a society, showing how regional customs and philosophical systems have developed in relation to weather. It will argue that better integration between the two approaches is needed if we are to fully grasp the interdependence of people and climate, a question that becomes ever more critical as we move further into the Anthropocene. This article is categorized under:Climate, History, Society, Culture > World Historical Perspectives [ABSTRACT FROM AUTHOR]

The extreme temperature changes under a 0.5 °C global mean surface temperature warming increment is of great importance for climate change adaption and risk management on post-Paris-Agreement agenda. The impacts of the already happened 0.5 °C warming increment on extreme temperature can serve as essential references for the 1.5/2 °C projections. Quantifying the observed changes of climate extremes is hampered by the limitation of observational datasets in both spatial coverage and temporal continuity. The reanalysis datasets are hoped to be useful substitutes for the observations, but their performance over continental China remains unknown. In this study, we compare the extreme temperature changes associated with the past 0.5 °C warming derived from three reanalysis datasets including JRA-55, ERA and 20CR with the observation in China. Distinct increases (decreases) in warm (cold) extremes are detected in all three reanalyses in a spatially aggregated perspective as in the observation. On regional scales the reanalyses have evident spreads in regions with insufficient observational coverage such as the western China. JRA-55 shows good agreement with the observation in both spatial patterns and magnitudes of extreme temperature changes. Both ERA and 20CR show weaker consistency with the observation, particularly in western China, mainly due to less observational constraints in data assimilation. The different aerosol data used in reanalysis assimilation systems also influenced the data quality. Our results indicate that while the reanalyses can serve as useful substitutes to fill in the observational gaps, cautious should be taken in regions with sparse observations and large anthropogenic aerosol emissions. [ABSTRACT FROM AUTHOR]

Grain price volatility during historical periods is regarded as an important indicator of the impact of climate change on economic system, as well as a key link to adjust food security and social stability. The present study used the wheat prices in Baoding Prefecture, China, during 1736–1850 to explore connections between climatic transition and grain price anomalies in the North China Plain. The main findings were as follows: (1) The grain price change showed an apparent correspondence with climatic transition. The period 1781–1820 was a transition phase, with more extremes and decreased precipitations when the climate shifted from a warm phase to a cold one. Corresponding with the climatic transition, the grain price during 1781–1820 was characterized by that the mean of the original grain price series was significantly higher (lower) than the previous (later) phase, and the variance and anomaly amplitude of the detrended grain price series was the highest during 1736–1850. (2) The correspondence between grain price extremes and drought events occurred in phases. Five grain price extremes occurred following drought events during 1781–1810, while extreme droughts were the direct cause of the grain price spike during 1811–1820. (3) Social stability affected by climate change also played an important role in the grain price spike between 1811 and 1820. Paralleling the pathway of "precipitation-grain production-grain price", climate change could have an impact on grain price via the pathway of "precipitation-grain production-grain price-famine-uprising-grain price", as shown during the Tianli Uprising in 1813. These findings could contribute to an improved understanding of the interaction between climate change and human society during the historical period. [ABSTRACT FROM AUTHOR]

Projecting climate impacts of a half‐degree warming increment is of high priority on post Paris Agreement science agendas. As the real world has already witnessed a 0.5°C global mean surface temperature warming increment, the observed climate changes associated with the half‐degree warming may be analogues for future projections. This hypothesis is examined by comparing the heat extreme changes in China derived from the observational records to projections of the Community Earth System Model (CESM) low‐warming experiment, which is the first short‐term stabilized simulation dedicated for the 1.5 and 2°C warming targets. The results of the CESM historical simulations are also evaluated. From the perspective of spatially aggregated, the heat extreme changes in China under the historical 0.5°C warming are detectable in observations. The nighttime extremes manifest more significant increase than daytime extremes. Heat extreme changes under the past half‐degree warming increment are reasonably captured in the historical simulations with slightly weaker magnitudes. The changes in the intensity indices in observations are better reproduced by the historical simulations than the frequency and duration indices. For the heat extreme changes in the future 0.5°C warming, the observational records can serve as conservative analogues in daytime extremes, while the nighttime extreme indices show comparable or weaker changes. The future reduction of anthropogenic aerosol emissions will amplify the increase of heat extremes in comparison to present day especially during daytime in China. Given the possibly intensified extremes associated with future aerosol reductions, more attention should be paid to the currently heavy polluted regions. Plain Language Summary: Since the Paris Agreement called for limiting the future global warming under 2°C and pursuing efforts to limit under 1.5°C, great efforts have been devoted to exploring the benefits of the 0.5°C less warming on natural and social systems. Since the world has already witnessed a 0.5°C warming increment, whether the associated observed climate changes can be analogues for future projections is an interesting and important question. Focusing on the heat extreme changes over continental China, we revisit the observational data sets to evaluate the performance of the Community Earth System Model (CESM) historical simulations and then compare the observed changes with the CESM 1.5°C/2°C future projections. We find that the heat extreme changes under the past 0.5°C warming increment are detectable in observations and can be well reproduced by the historical simulations. The changes of intensity indices in the observations are better reproduced by the model than the frequency and duration indices. For the daytime heat extremes, the observations only give the lower boundary for the projected changes in future 0.5°C warming. China may experience severer increase in daytime heat extremes due to possible aerosol reductions in the future. Our results would provide useful information to climate change adaption and risk management. Key Points: The increases of the general heat extremes under the past 0.5°C warming increment in the observational records are detectable in ChinaThe CESM historical simulations can reproduce the changes of the heat extremes in China but with slightly weaker magnitudesThe daytime extreme changes in observations can serve as conservative analogues for the future projections [ABSTRACT FROM AUTHOR]

Climate change and urbanization collectively influence precipitation changes. However, their separate potential contributions to precipitation changes are not well understood due to their complex interactions. Hence, a "trajectory"‐based method was used to separate their potential contributions across national, regional and local scales in China. Precipitation changes in non‐urban regions can be regarded as representing the influence of climate change and can serve as a reference for isolating precipitation changes due to urbanization in urban areas. Our results revealed that climate change was the dominant factor for precipitation trends, while urbanization exhibited a relatively weak influence, especially for extreme precipitation at the national scale. At the regional scale, the impacts of urbanization on precipitation became more significant. About 20.2 and −30.6% of positive and negative trends in total precipitation originated from urbanization. At the local scale, the potential contribution of urbanization was strongly correlated with local environmental characteristics. Although there were differences in the potential contributions of climate change and urbanization at national, regional and local scales, climate change was the dominant factor for precipitation trends and urbanization acted as a regulator to drying or wetting due to precipitation under climate change. In general, urbanization causes a greater impact on total precipitation than on precipitation extremes. Due to this, attribution approach is static and broad‐based and does not render the level of confidence that is needed for scale‐aware attribution, future studies are needed to understand the physical mechanisms of impacts of local environment changes on precipitation trends at different geographical locations over China. [ABSTRACT FROM AUTHOR]

This study examines potential benefits of performance‐based multi‐model ensembles (MMEs) in projecting the impacts of climate change on extreme precipitation indices over East Asia (EA) using the data from 19 GCMs in the coupled model intercomparison project 5 (CMIP5). The Taylor skill score is adopted as the measure of the model skills in simulating the spatial and interannual variability of the selected extreme precipitation indices over four EA regions. The overall rank based on the total skill score (TSC) is used to construct two skill‐based MMEs, MME of high‐skill, MMH (MME of low‐skill, MML) that include the top (bottom) seven models, in addition to the simple ensemble of all 19 GCMs (ENS). Inter‐GCM consistency is measured using the signal‐to‐noise ratio (SNR). In the present‐day period, MMH yields higher skill scores than MML and ENS for almost all extreme precipitation indices as well as regions. Regional variations in biases, inter‐model consistency, and TSC are large. The inter‐model consistency is highest for Northern China and Manchuria and is lowest for Southern China. The most notable differences in the key properties of climate change signals from the three MMEs among the three ensembles are that the climate change signals from MMH and ENS exceed the 90% significance level in much larger areas than those from MML. However, the differences in the climate change signals between MMH and MML are generally below the 90% significance level. The SNR of the projected climate change signals shows that MMH yields more consistent climate change signals than ENS/MML. Both the SNR differences and the area in which statistical significance exceed the 90% level suggest that constructing climate change signals from a group of higher‐skill models may yield more reliable projections than constructing MMEs from the entire models or a group of lower‐skilled models. The domains used in the study (SOUTH: 110°–140°E, 20°–30°N, MIDDLE: 110°–140°E, 30°–40°N), NORTH: 110°–140°E, 40°–50°N, EA: 110°–140°E, 20°–50°N) [ABSTRACT FROM AUTHOR]

ABSTRACT: The changes in mean and extreme climate in China during 2020–2060 are detected with both Weather Research and Forecasting and RegCM4, by downscaling the simulations from EC‐EATTH and IPSL‐CM5A under both the RCP4.5 and RCP8.5 scenarios. The climate changes under the two scenarios exhibit similar patterns, with stronger intensity under the RCP8.5 scenario. For the mean precipitation, increases are projected in most regions, with the largest relative increase in the Tarim Basin. Slight drought mainly occurs in the south‐eastern part of China. The frequency of drizzle rain is expected to decrease in all the sub‐regions, but the moderate to heavy rainfall as well as the storm would occur more frequently, especially on the Tibetan Plateau. The whole country would experience much warmer climate in the future, with the strongest warming over the Tibetan Plateau. By detecting the changes in climate extremes, it is indicated that less dry extremes would occur in the wet areas of China, while more dry events in the arid and semiarid regions. The wet extreme indices would increase in most regions, especially in the wet areas. The surface air temperature tends to become extremely warmer in the future over the whole country, with the strongest change over the Tibetan Plateau. The changes in mean and extreme climate depend strongly on the driving global climate models, with wetter and warmer climate in the downscalings over IPSL‐CM5A, and the model physics of the regional climate models also exert great impact on the projections. Finally, the possible mechanisms for the changes of extreme precipitation are discussed. The enhanced summer monsoon in the future transports more moisture to China, which could lead to more summer precipitation. As a result, the wet extremes tend to increase. [ABSTRACT FROM AUTHOR]

Under the exacerbation of climate change, climate extreme events, especially for drought, happened frequently and intensively across the globe with greater spatial differences. We used the Standardized Precipitation-Evapotranspiration Index computed from the routine meteorological observations at 269 sites in Southwest China (SWC) to study the drought characteristics (e.g., extent, duration and intensity) and their decadal variations during 1971-2012. It was revealed that the drought, in responses to the coupling between decadal precipitation and potential evapotranspiration (PET) anomalies, differed among regions and periods. For the entire SWC, droughts in 1970s and 2000s+ was generally stronger than in 1980s and 1990s with respect to their spatial extent, duration and intensity, especially in 2000s+. It was well-known that drought was closely related with a lack of precipitation; however, the impact of atmospheric demand of evaporation (reflected by PET here) on drought (e.g., duration and intensity) was rarely paid enough attentions. To that end, a spatial multi-linear regression approach was proposed in this study for quantifying the contributions of decadal PET and precipitation variations to drought duration and intensity. We have found that the contributions of decadal PET anomalies to drought duration and intensity could exceed those of precipitation, e.g., during 1980s and 1990s in SWC. Additionally, despite the strongest droughts in 2000s+, it was suggested that PET could exert comparable impacts on drought anomalies as precipitation. All these findings implied that PET plays a critical role in drought event, which acts to amplify drought duration and intensity. To sum up, this study stressed the need for enough attentions for PET processes in drought studies. [ABSTRACT FROM AUTHOR]

This study analyzes the variation and risk changes of extreme precipitation under nonstationarity conditions using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) and the Mann-Kendall (MK) test. The extreme precipitation series is extracted from the observations during the second flood season (July-September) from 1960 to 2012 derived from 86 meteorological stations in the southeastern coastal region of China. The trend of mean (Mn) and variance (Var) of extreme precipitation is detected by MK. Ten large-scale circulation variables and four greenhouse gases are selected to construct a climate change index and a human activity index, which are based on principal component analysis. The recurrence risk of extreme precipitation is calculated by GAMLSS while considering climate changes and human activities. The results demonstrate that the nonstationarity characteristic of extreme precipitation is widespread in this region. A significant increasing trend of Mn is found in Shanghai, eastern Zhejiang, and northern and southern Fujian. An enhanced Var is found in eastern Guangdong. A significant positive correlation exists between climate changes/human activities and Mn/Var, especially in Zhejiang and Fujian. Generally, the contribution of climate changes and human activities to Mn is greater than it is to Var. In this region, the precipitation amount of high-frequency (2-yr return period) and low-frequency (100-yr return period) events increases from inland to coastal and from north to south. The government should pay careful attention to these trends because the intensity of extreme precipitation events and their secondary disasters could result in serious losses. [ABSTRACT FROM AUTHOR]