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Serving as ‘the water tower of Asia’, the Tibetan Plateau (TP) supplies water resources to more than 1.4 billion people. It is warming more rapidly than the global average over the past decades, affecting regional hydrological cycle and ecosystem services. However, the anthropogenic (ANT) influence remains unknown. Here we assessed the human contribution to the observed TP warming based on coupled climate simulations and an optimal fingerprinting detection and attribution analysis. We show that the observed rapid warming on the TP (1.23 °C over 1961–2005) is attributable to human influence, and particularly, to the greenhouse gases with a contribution of 1.37 °C by the best estimate, which was slightly offset by anthropogenic aerosols. As the multi-model ensemble tends to underestimate the ANT warming trend, the constraint from the attribution results suggests an even warmer future on the TP than previously expected, implying further increased geohazard risks in the Asian water tower.

The Paris Agreement set a goal to keep global warming well below 2 °C and pursue efforts to limit it to 1.5 °C. Understanding how 0.5 °C less warming reduces impacts and risks is key for climate policies. Here, we show that both areal and population exposures to dangerous extreme precipitation events (e.g., once in 10- and 20-year events) would increase consistently with warming in the populous global land monsoon regions based on Coupled Model Intercomparison Project Phase 5 multimodel projections. The 0.5 °C less warming would reduce areal and population exposures to once-in-20-year extreme precipitation events by 25% (18–41%) and 36% (22–46%), respectively. The avoided impacts are more remarkable for more intense extremes. Among the monsoon subregions, South Africa is the most impacted, followed by South Asia and East Asia. Our results improve the understanding of future vulnerability to, and risk of, climate extremes, which is paramount for mitigation and adaptation activities for the global monsoon region where nearly two-thirds of the world’s population lives. The populous global land monsoon region has been suffering from extreme precipitation. Here, the authors show that limiting global warming to 1.5 °C instead of 2 °C could reduce areal and population exposures to baseline once-in-20-year rainfall extremes by 25% (18–41%) and 36% (22–46%), respectively.

In summer 2018, an extraordinary heat wave with record-breaking high temperatures hit Northeast Asia. However, the contribution of atmospheric circulation to this heat wave remains unknown. In this study, we quantify the contribution of circulation by using the flow analogue method. It is found that Northeast China, Korea and Japan were the most affected areas by the heat event, from daily to monthly timescales. The persistent high temperature was associated with an anticyclonic anomaly over Northeast Asia, related to the record-breaking northward shift of the western Pacific subtropical high (WPSH). The persistent anomalous anticyclone played a dominant role in this heat event, explaining half of the magnitude of the heat event. Both thermodynamical change and dynamical change in recent decades have increased the probability of occurrence of this kind of heat event over Northeast Asia. Specifically, the change in dynamical flow explains a fraction of less than 20% of the increases in probability of heat events. The contribution of thermodynamical changes to heat events generally increases with the rarity of the extreme event.

During summer 2021, Western North America (WNA) experienced an unprecedented heatwave with record-breaking high temperatures associated with a strong anomalous high-pressure system, i.e., a heat dome. Here, we use a flow analog method and find that the heat dome over the WNA can explain half of the magnitude of the anomalous temperature. The intensities of hot extremes associated with similar heat dome-like atmospheric circulations increase faster than background global warming in both historical change and future projection. Such relationship between hot extremes and mean temperature can be partly explained by soil moisture-atmosphere feedback. The probability of 2021-like heat extremes is projected to increase due to the background warming, the enhanced soil moisture-atmosphere feedback and the weak but still significantly increased probability of the heat dome-like circulation. The population exposure to such heat extremes will also increase. Limiting global warming to 1.5 °C instead of 2 °C (3 °C) would lead to an avoided impact of 53% (89%) of the increase in population exposure to 2021-like heat extremes under the RCP8.5-SSP5 scenario. The heat dome explains about 55% of the 2021 Western North American high temperatures. The intensity of heat extremes associated with such circulations are increasing faster than background global warming due to soil moisture feedbacks.

High Mountain Asia (HMA) has experienced a spatial imbalance in water resources in recent decades, partly because of a dipolar pattern of precipitation changes known as South Drying–North Wetting 1 . These changes can be influenced by both human activities and internal climate variability 2 , 3 . Although climate projections indicate a future widespread wetting trend over HMA 1 , 4 , the timing and mechanism of the transition from a dipolar to a monopolar pattern remain unknown. Here we demonstrate that the observed dipolar precipitation change in HMA during summer is primarily driven by westerly- and monsoon-associated precipitation patterns. The weakening of the Asian westerly jet, caused by the uneven emission of anthropogenic aerosols, favoured a dipolar precipitation trend from 1951 to 2020. Moreover, the phase transition of the Interdecadal Pacific Oscillation induces an out-of-phase precipitation change between the core region of the South Asian monsoon and southeastern HMA. Under medium- or high-emission scenarios, corresponding to a global warming of 0.6–1.1 °C compared with the present, the dipolar pattern is projected to shift to a monopolar wetting trend in the 2040s. This shift in precipitation patterns is mainly attributed to the intensified jet stream resulting from reduced emissions of anthropogenic aerosols. These findings underscore the importance of considering the impact of aerosol emission reduction in future social planning by policymakers. Dipolar precipitation change in High Mountain Asia during summer is primarily driven by weakened westerly jet and decadal variations in the South Asian monsoon, and the dipolar pattern is projected to shift to a monopolar wetting trend in the 2040s.

Unlike traditional finance, digital inclusive finance is committed to integrating digital technology with the financial industry to bring groups originally excluded from traditional finance back into formal financial services and provide financial services at reasonable prices and matching needs for all social classes. Digital inclusive finance can effectively reduce the financing costs of SMEs, improve the external financing environment of enterprises, and provide more convenient, equal and perfect financial services for enterprises by using technical support such as \"big data + artificial intelligence\". The development level of digital inclusive finance is a classical multiple attributes group decision making (MAGDM). The Probabilistic hesitant fuzzy sets (PHFSs), which utilize the possible values and its possible membership degrees to depict decision-makers’ behavior in different conditions, has been paid great attention. Though numerous methods have been applied in this environment since PHFSs has been introduced, there are still new fields to be explored. In this paper, we introduce the Cumulative Prospect Theory TODIM (CPT-TODIM) for probabilistic hesitant fuzzy MAGDM(PHF-MAGDM). Meanwhile, the information of entropy is utilized to calculate the weight of attributes, which is used to improve the classical TODIM method. At last, we utilize a numerical case for evaluating the development level of digital inclusive finance to compare the extended CPT-TODIM method with the classical TODIM method.

Projected changes of future precipitation extremes exhibit substantial uncertainties among climate models, posing grand challenges to climate actions and adaptation planning. Practical methods for narrowing the projection uncertainty remain elusive. Here, using large model ensembles, we show that the uncertainty in projections of future extratropical extreme precipitation is significantly correlated with the model representations of present-day precipitation variability. Models with weaker present-day precipitation variability tend to project larger increases in extreme precipitation occurrences under a given global warming increment. This relationship can be explained statistically using idealized distributions for precipitation. This emergent relationship provides a powerful constraint on future projections of extreme precipitation from observed present-day precipitation variability, which reduces projection uncertainty by 20–40% over extratropical regions. Because of the widespread impacts of extreme precipitation, this has not only provided useful insights into understanding uncertainties in current model projections, but is also expected to bring potential socio-economic benefits in climate change adaptation planning. This study finds that projections of future extreme precipitation can be made more reliable using a constraint from observed present-day precipitation variability, which reduces projection uncertainty by 20–40% over the extra-tropics.

Eastern China experienced excessive Meiyu rainfall in the summer of 2020, with a long rainy season and frequent extreme rainfall events. Extreme rainfall occurred on daily to monthly time scales. In particular, persistent heavy rainfall events occurred; e.g., the maximum accumulated rainfall over four consecutive weeks (Rx28day) in the lower reaches of the Yangtze River was 94% greater than climatology, breaking the observational record since 1961. With ongoing anthropogenic climate change, it is vital to understand the anthropogenic influence on this extreme rainfall event and its driving mechanisms. In this study, based on multi-model simulations under different external forcings that participate in the Detection and Attribution Model Intercomparison Project (DAMIP) in the Coupled Model Intercomparison Project-phase 6 (CMIP6), we show that anthropogenic forcing has reduced the probability of the Rx28day extreme rainfall as that in observations in the lower reaches of the Yangtze River in 2020, by 46% (22–62%). Specifically, greenhouse gas (GHG) emissions have increased the probability by 44% as a result of atmospheric warming and moistening. However, this effect was offset by anthropogenic aerosols, which reduced the probability by 73% by reducing atmospheric moisture and weakening the East Asian summer monsoon circulation. With the continuous emissions of GHGs and reductions in aerosols in the future, similar persistent heavy rainfall events are projected to occur more frequently. A higher occurrence probability is expected under higher emission scenarios, which is estimated to be 4.6, 13.6 and 27.7 times that in the present day under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 emission scenarios, respectively, by the end of the 21st century. Thus, efficient mitigation measures will help to reduce the impacts related to extreme rainfall.

Extreme weather events and their consequential impacts have been a key feature of the climate in recent years in many parts of the world, with many partly attributed to ongoing global-scale warming. The past year, 2022, has been no exception, with further records being broken. The year was marked by unprecedented heatwaves and droughts with highly unusual spatial extent, duration and intensity, with one measure indicating an aggregated and overall intensity of extreme heat events worldwide not seen since at least 1950. The extreme drought measured by surface soil moisture covered 47.3% of global land areas in 2022, which was the second most widespread year since 1980. Here, we examine notable events of the year in five major regions of the world: China’s Yangtze River region, western Europe, the western U.S., the Horn of Africa and central South America. For each event, we review the potential roles of circulation, oceanic forcing (especially the “triple-dip” La Niña) and anthropogenic climate change, with an aim of understanding the extreme events in 2022 from a global perspective. This will serve as a reference for mechanism understanding, prediction and attribution of extreme events.

To identify potential categories of alarm fatigue among ICU nurses and to explore the differences in characteristics and influencing factors among different categories. Using convenience sampling, 597 ICU nurses from 12 tertiary public hospitals across 8 cities in the Inner Mongolia Autonomous Region of China were recruited from September 2024 to December 2024. A cross-sectional survey was conducted using the General Information Questionnaire, ICU Nurses' Alarm Fatigue Scale, Stanford Presenteeism Scale: Health Status and Employee Productivity, and Nurses' Emotional Labor Scale. Potential profiles of nurse alarm fatigue were analyzed, and the influencing factors of different profiles were explored by univariate analysis and multivariate logistic regression analysis. The median alarm fatigue scale score was 26(IQR = 19.75-31), and the alarm fatigue of ICU nurses could be categorized into low fatigue-robust tolerance group (30.8%), moderate fatigue (54.4%), and high fatigue-negative coping group (14.9%). The regression analyses showed that the number of children, the frequency of night shifts, the health status and employee productivity score, and the emotional labor score were the main factors of the ICU factors influencing different potential categories of nurse alarm fatigue (P < 0.05). ICU nurses alarm in Inner Mongolia exhibited moderate-to-high alarm fatigue with notable subgroup heterogeneity. Nursing managers should implement tailored interventions addressing profile-specific factors, such as workload adjustments and emotional support strategies, to mitigate alarm fatigue.