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Precipitation whiplash, including abrupt shifts between wet and dry extremes, can cause large adverse impacts on human and natural systems. Here we quantify observed and projected changes in characteristics of sub-seasonal precipitation whiplash and investigate the role of individual anthropogenic influences on these changes. Results show that the occurrence frequency of global precipitation whiplash is projected to be 2.56 ± 0.16 times higher than in 1979–2019 by the end of the 21 st Century, with increasingly rapid and intense transitions between two extremes. The most dramatic increases of whiplash show in the polar and monsoon regions. Changes in precipitation whiplash show a much higher percentage change than precipitation totals. In historical simulations, anthropogenic greenhouse gas (GHG) and aerosol emissions have increased and decreased precipitation whiplash occurrences, respectively. By 2079, anthropogenic GHGs are projected to increase 55 ± 4% of the occurrences risk of precipitation whiplash, which is driven by shifts in circulation patterns conducive to precipitation extremes. This study shows that the occurrence frequency of global precipitation whiplash is projected to be ~2.6 times higher by the end of the 21st century compared to 1979–2019, with increasingly rapid and intense transitions between the two extremes.

Understanding the variability of spatial extents of precipitation extremes favors an accurate assessment of the severity of disasters caused by extreme precipitation events. Using a restricted neighborhood method, we identify the spatial extents of global precipitation extremes over 1983–2018 and examine their spatiotemporal variability and associated changes. Results show that the mid-latitudes shows the largest spatial extent of precipitation extremes, and the spatial extents in non-tropical regions over the Northern Hemisphere show significant seasonal differences. In non-monsoon regions, the spatial extents of precipitation extremes in autumn and winter are larger than those in spring and summer, and the annual average spatial extents of precipitation extremes all exceed 500 km, which are larger than those in monsoon regions. All the five non-monsoon regions over the Northern Hemisphere and three monsoon regions in the western Pacific show statistically significant increases in the spatial extent of precipitation extremes in most seasons.

Changes in precipitation seasonality or redistribution of precipitation could exert significant influences on regional water resources availability and the well-being of the ecosystem. However, due to the nonuniform distribution of precipitation stations and intermittency of precipitation, precise detection of changes in precipitation seasonality on the global scale is absent. This study identifies and inter-compares trends in precipitation seasonality within seven precipitation datasets during the past three decades, including two gauge-based datasets derived from the Climatic Research Unit (CRU) and the Global Precipitation Climatology Centre (GPCC), one remote sensing-retrieval obtained from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), three reanalysis datasets obtained from National Centers for Environmental Prediction reanalysis II (NCEP2), European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and Modern Era Reanalysis for Research and Applications Version 2 (MERRA2), and one precipitation dataset merged from above three types, Multi-Source Weighted Ensemble Precipitation Version 1.2 (MSWEP_V1.2). Values of two indices representing the precipitation seasonality, the normal seasonality index (SI) and the dimensionless seasonality index (DSI), are estimated for each land grid in each precipitation dataset. The results show that DSI is more sensitive to changes in the temporal distribution of precipitation as it considers both annual amount and monthly fluctuations of precipitation, compared to SI that only considers monthly fluctuations of precipitation. There are large differences in precipitation seasonality at annual and climatologic scales between precipitation datasets for both SI and DSI. Within the seven precipitation datasets, PERSIANN-CDR SI and DSI show high precipitation seasonality while CRU SI, and ERA-Interim and MERRA2 DSI show the low precipitation seasonality in all continental regions. During 1988–2013, PERSIANN-CDR, NCEP2 and ERA-Interim show more widespread, statistically significant trends in precipitation seasonality than other four precipitation datasets. PERSIANN-CDR and NCEP2 show statistically significant decreases in SI over Middle East and Central Asia, while ERA-Interim, MERRA2 and MSWEP_V1.2 SI increase over Central and South Africa. Increases in SI over the most of South America are significant. Regions of Canada/Greenland/Iceland, East and South Africa show significant increases in precipitation seasonality, while South Europe/Mediterranean and Central Africa show significant decreases in precipitation seasonality in most datasets. Although time series of seasonality indices values fluctuate correlatively in recent three decades, there are no regions on which all precipitation datasets show a consistent, statistically significant, positive or negative trend in indices of precipitation seasonality. These inconsistent changes in precipitation seasonality within various precipitation datasets imply the importance of choosing dataset when studying changes in regional precipitation seasonality.

Analyzing the spatiotemporal characteristics and causes of landscape pattern changes in watersheds around big cities is essential for understanding the ecological consequence of urbanization and provides a basic reference for the watershed management. This study used a land-use transition matrix and landscape indices to explore the spatiotemporal change of land use and landscape pattern over Liuxihe River basin of Guangzhou in the southeast of China from 1980 to 2015 with multitemporal Landsat satellite data in response to the rapid urbanization process. Primary temporal and spatial influencing factors were first quantitatively identified through grey relation analysis (calculating correlation degree between land use changes and influencing factors) and Geodetector (detecting landscape spatial heterogeneity and its driving factors), respectively. Considerable spatial and temporal differences in land use and landscape pattern changes were observed herein, thus determining the influencing factors of these differences in the Liuxihe River basin. These changes were characterized by a large increase in construction land converted from cropland, particularly in the middle and lower reaches of the basin from 2000 to 2010, causing dramatic fragmentation and homogenization of the landscape pattern there. Meanwhile, the landscape pattern gradually transitioned from an agricultural land use dominant landscape to a construction land use dominant landscape in these regions. Furthermore, the rapid growth of a nonagricultural population and the transformation of industry primarily caused the temporal changes of landscape pattern, and the landscape spatial heterogeneity was mainly caused by the interaction of complicated geomorphology and anthropogenic activities in different spatial locations, particularly after 2000. This study not only provides an improved approach to quantifying the main spatiotemporal influencing factors of landscape pattern changes during different time periods, but also offers a reference for decision-makers to formulate optimal strategies on ecological protection and urban sustainable development of different regions in this study area.

This study inter-compares the spatiotemporal features of precipitation extremes at global and regional scales within six daily precipitation datasets, i.e., gauge-based (Global Precipitation Climatology Center dataset, GPCC), satellite-retrieval (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR), three reanalysis datasets (ERA-Interim, ERAI; the National Center for Atmospheric Research Reanalysis 2, NCEP2; and the WATCH-Forcing-Data-ERA-Interim, WFDEI), and products merged from the three type datasets (the Multi-Source Weighted-Ensemble Precipitation, MSWEP). All datasets reproduce similar spatial patterns of both annual and seasonal precipitation extremes over the period from 1979 to 2017. Compared to the reference dataset gauge-based GPCC, the reanalysis WFDEI outperforms among six products with spatial correlation coefficients of 0.89 and 0.80 for the annual extreme indices (i.e., annual total amount of 95th precipitation and maximum 1-day precipitation), respectively. The satellite-based product PERSIANN-CDR performs better than reanalyses and merged datasets in capturing the temporal variability of the intensity and amount of precipitation extremes with similar changing tendencies and magnitudes of about 45 mm day−1 and 230 mm at the global scale, respectively. The reanalyses and merged products underestimate the intensity of precipitation extremes. The selected six datasets behave differently in various regions. For the percentile-based frequency of precipitation extremes, NCEP2 performs well in regions of the Southeast Asian (SEA) and Amazon (AMZ), while WFDEI better matches GPCC over East North America (ENA) and North Australia (NAU) in both spatial patterns and temporal changes with correlation coefficients of 0.84 and 0.90, respectively. For the intensity features of annual precipitation extremes, NCEP2 performs better than other four datasets over regions of SEA, AMZ and West Africa (WAF). ERAI and WFDEI are consistent with GPCC in ENA and NAU with correlations coefficients of the intensity between ERAI (WFDEI) and GPCC are 0.82 (0.77) and 0.78 (0.64) for ENA and NAU, respectively. For the intensity of seasonal precipitation extremes, GPCC shows the highest estimates in regions of SEA, AMZ, ENA and WAF. ERAI and WFDEI perform better in reproducing the spatial patterns of seasonal precipitation extremes in all regions. NCEP2 (ERAI and WFDEI) show(s) consistent temporal variability of seasonal precipitation extremes with GPCC in regions of AMZ and WAF (ENA and NAU). Overall, there are large discrepancies in the absolute values of daily precipitation among datasets, and performances of non-gauged-based precipitation datasets in capturing the spatiotemporal variability of precipitation extremes are dependent on seasons, regions, and time periods.

Comprehending the causal relationships between shifting climatic patterns, dynamic vegetation cover, and altered hydrological cycles constitutes a fundamental prerequisite for developing adaptive strategies in water resources governance. We analyze changes in baseflow (BF) and stormflow (SF) in 3,388 watersheds globally in the response to changes in precipitation characteristics, vegetation (Normalized Difference Vegetation Index, NDVI), temperature, potential evapotranspiration, and the aridity index from 1982 to 2016, based on Random Forest Model simulations and Shapley Additive Explanations. We also evaluate the effects of anthropogenic climate change on changes in baseflow index (BFI, the ratio of baseflow to total streamflow) using the signal‐to‐noise ratio (SNR) method applied to multi‐model simulations from ISIMIP2b. Results show that the BF and SF in the majority of watersheds show consistent trends, but they also show the opposite trends in specific seasons. Over 60% of watersheds in the Northern Hemisphere experienced a decrease in BF contribution to the total streamflow during the June‐July‐August season. Minimum temperature and NDVI are the most important factors influencing changes in BF and SF. Minimum and maximum temperature, precipitation seasonality, and precipitation intensity show negative effects on the BF, and the impact of vegetation on the BFI varies from region to region. The contribution of BF to the total streamflow decreases significantly at the global scale under the impact of anthropogenic climate change, according to the SNR analysis. The interaction of climate and vegetation changes on changes in BF and SF should take into account to regional adaptation of climate change for the utilization of groundwater and surface water resources.

Sustainable management of blue and green water resources is vital for the stability and sustainability of watershed ecosystems. Although there has been extensive attention paid to blue water (BW), which is closely related to human beings, the relevance of green water (GW) to ecosystem security is typically disregarded in water resource evaluations. Specifically, comprehensive studies are scarce on the detection and attribution of variations of blue and green water in the Dongjiang River basin (DRB), an important source of regional water supply in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. Here we assess the variations of BW and GW scarcity and quantify the impacts of climate change and land use change on BW and GW in DRB using the multi-water-flux calibrated Soil and Water Assessment Tool (SWAT). Results show that BW and green water storage (GWS) in DRB increased slowly at rates of 0.14 and 0.015 mm a−1, respectively, while green water flow (GWF) decreased significantly at a rate of −0.21 mm a−1. The degree of BW and GW scarcity in DRB is low, and the per capita water resources in more than 80 % of DRB exceed 1700 m3 per capita per year. Attribution results show that 88.0 %, 88.5 %, and 39.4 % of changes in BW, GWF, and GWS result from climate change. Both climate change and land use change have decreased BW, while climate change (land use change) has decreased (increased) GWF in DRB. These findings can guide the optimization of the allocation of blue and green water resources between upper and lower reach areas in DRB and further improve the understanding of blue and green water evolution patterns in humid regions.

Along with the development of remote sensing technology, the spatial–temporal variability of vegetation productivity has been well observed. However, the drivers controlling the variation in vegetation under various climate gradients remain poorly understood. Identifying and quantifying the independent effects of driving factors on a natural process is challenging. In this study, we adopted a potent machine learning (ML) model and an ML interpretation technique with high fidelity to disentangle the effects of climatic variables on the long-term averaged net primary productivity (NPP) across the Amazon rainforests. Specifically, the eXtreme Gradient Boosting (XGBoost) model was employed to model the Moderate-resolution Imaging Spectroradiometer (MODIS) NPP data, and the Shapley addictive explanation (SHAP) method was introduced to account for nonlinear relationships between variables identified by the model. Results showed that the dominant driver of NPP across the Amazon forests varied in different regions, with temperature dominating the most considerable portion of the ecoregion with a high importance score. In addition, light augmentation, increased CO2 concentration, and decreased precipitation positively contributed to Amazonia NPP. The wind speed for most vegetated areas was under the optimum, which benefits NPP, while sustained high wind speed would bring substantial NPP loss. We also found a non-monotonic response of Amazonia NPP to VPD and attributed this relationship to the moisture load in Amazon forests. Our application of the explainable machine learning framework to identify the underlying physical mechanism behind NPP could be a reference for identifying relationships between components in natural processes.

Background The heterogeneous subtypes in dilated cardiomyopathy (DCM) are poorly characterized, thus posing challenges to risk stratification. This study aimed to establish a DCM subtype framework based on metabolic and immunoinflammatory factors. Methods DCM subtypes were identified using unsupervised clustering based on the expression patterns of metabolism-related genes in the left ventricular myocardium of 89 DCM patients. By comparing metabolic pathways, clinical characteristics, immune cell infiltration, inflammatory responses, and immunotherapy efficacy between the subtypes, key metabolic genes were identified through correlation analysis and validated at both bulk and single-cell levels. The alterations in gene expression were verified using the DCM mouse model. Molecular docking was performed to assess the binding affinity between the target protein and potential therapeutic small molecules. Results Two subtypes were identified; subtypes 1 and 2 were characterized by increased amino acid metabolism and decreased glucose and energy-related metabolisms, respectively. Subtype 2 displayed worse left ventricular structure and function, higher levels of immune and inflammatory activity, and a more favorable response to immunotherapy. The integrative analysis identified DHRS7C as a key regulator of glucose/energy metabolism; its expression was inversely correlated with left ventricular impairment. The DCM mice showed downregulated DHRS7C expression, which positively correlated with cardiac dysfunction. Additionally, molecular docking identified 17beta-estradiol as a potential therapeutic agent targeting DHRS7C. Conclusions This study suggested two heterogeneous DCM subtypes with different metabolic and immunoinflammatory profiles. Furthermore, DHRS7C was inversely correlated with DCM indices and could be targeted by 17beta-estradiol. Graphical abstract

Floods in river deltas are driven by complex interactions between astronomical tides, sea levels, storm surges, wind waves, rainfall‐runoff, and river discharge. Given the anticipated increase in compound flood hazards in river deltas in a warming climate, climate‐informed regional to local extreme water levels (EWLs) is thus critical for decision‐makers to evaluate flood hazards and take adaptation measures. We develop a simple yet computationally efficient stress test framework, which combines historical and projected climatological information and a state‐of‐the‐art hydrodynamic model, to assess future compound coastal‐fluvial flood hazards in river deltas. Our framework is applied in the world's largest single urban area, China's Pearl River Delta (PRD), which is also characterized by densely crossed river network. We find that extreme sea level is the dominant driver causing the compound coastal‐fluvial flood in the PRD over the past 60 years. Meanwhile, there is large spatial heterogeneity of the individual and compound effects of the typhoon intensity, local sea‐level rise, and riverine inflow on coastal‐fluvial floods. In a plausible disruptive scenario (e.g., a 0.50 m sea‐level rise combined with a 9% increase in typhoon intensity in a 2°C warming), the EWL will increase by 0.76 m on average. An additional 1.54 and 0.56 m increase in EWL will occur in the river network and near the river mouth, respectively, if coastal floods coincide with the upstream mean annual flood. Findings from our modeling framework provide important insights to guide adaptation planning in river deltas to withstand future compound floods under climate change. Plain Language Summary Compound floods pose serious threats to the dense population living in river deltas worldwide. Climate change could increase the compound flood hazards through stronger tropical cyclones, sea level rise, higher extreme precipitation, and river flows. It is urgent to understand how the so‐called grey swan events will evolve under a changing climate in these already flood‐prone areas. Here, grey swan events are high‐consequence events that are beyond people's experience but may be foreseeable and thus can be systematically prepared for. In this study, a storyline‐based framework is proposed to assess the potential grey swan compound floods under a warming climate. We apply this framework to China's Pearl River Delta, the world's largest single urban area. In a plausible disruptive scenario (e.g., a 0.50 m sea‐level rise combined with a 9% increase in typhoon intensity in a 2°C warming), we find that extreme water levels will increase by 0.76 m on average. Our flexible and computationally efficient storyline‐based framework could guide coastal planners to prepare for the grey swan compound flood hazards under climate change. Key Points A quantitative stress test framework is proposed to assess the compound coastal‐fluvial flood in the river delta at minimal cost A state‐of‐the‐art unstructured mesh generation technology is applied to develop an objective and reproducible hydrodynamic model Compound effects of typhoon intensity, local sea‐level rise, and river inflow on coastal‐fluvial floods are systematically investigated