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The results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Center is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS. Plain Language Summary: We investigate how human‐induced emissions affect daily rainfall extremes across the United States. The attribution of an extreme event to human‐induced emissions depends on the selected extreme event statistics, with setting a threshold to define what counts as an extreme event remaining a major challenge. To overcome this, we used the Extended Generalized Pareto Distribution (ExtGPD) that jointly models both low and heavy rainfall events without defining a threshold, providing a more complete picture of the full distribution including extremes. We fitted the ExtGPD to 12 general circulation models and compared scenarios with and without human‐induced emissions. Our findings suggest that human emissions are responsible for the observed intensity of daily rainfall extremes across the United States, especially in regions with temperate climates, and that these extremes would have been smaller without greenhouse gases. Key Points: We apply the Extended Generalized Pareto Distribution for probabilistic event attribution to bypass issues with threshold specificationAnthropogenic emissions alone could exacerbate the observed magnitude of extreme daily precipitation across the United StatesThe study underscores the urgent need for mitigation, revealing a clear link between anthropogenic activities and extreme precipitation [ABSTRACT FROM AUTHOR]

In this study, future precipitation and drought in the Ca river basin, Vietnam, were projected based on an ensemble of 27 CMIP6 models for four climate change scenarios. The impact of climate change on precipitation and drought was investigated. Monthly precipitation observation data were adjusted using the bias correction method. To detect drought events, the Standard Precipitation Index (SPI) was employed. Changes in drought were assessed using SPI3, SPI6, and SPI12. Although the amount of annual total precipitation slightly increased, the drought events may become more severe. There is a high likelihood of increased drought intensity and severity in Vietnam due to climate change. The frequency of droughts is likely to change depending on the location and climate change scenario. We found that the frequency and severity of droughts may be altered depending on the window size of SPI. The short-term drought events will be more frequent and severe, and long-term drought events will become more severe in the Ca river basin. [ABSTRACT FROM AUTHOR]

Extreme flood events have regional differences in their generating mechanisms due to the complex interaction of different climate and catchment processes. This study aims to examine the capability of climate drivers to capture year‐to‐year variability in global flood extremes. Here, we use a statistical attribution approach to model seasonal and annual maximum daily discharge for 7,886 stations worldwide, using season‐ and basin‐averaged precipitation and temperature as predictors. The results show robust performance of our seasonal climate‐informed models in describing the inter‐annual variability in seasonal and annual maximum discharges regardless of the geographical region, climate type, basin size, degree of regulation, and impervious area. The developed models enable the assessment of the sensitivity of flood discharge to precipitation and temperature changes, indicating their potential to reliably project changes in the magnitude of flood extremes. Plain Language Summary: Successfully modeling the changes in magnitudes of flood events is challenging due to the complexity of flood‐driving processes, which vary depending on region and season. Here, we examine how seasonal precipitation and temperature can describe changes in the flood magnitudes. Although we only consider these basic climate factors as predictors, our climate‐informed models consistently show a good performance in capturing the year‐to‐year variability in the magnitude of seasonal and annual flooding events across 7,886 stations worldwide. These results show the suitability of seasonal precipitation and temperature as proxies for different climate and catchment processes leading to flooding. Key Points: For 7,886 stations across the globe, we develop statistical attribution models for seasonal and annual maximum daily discharge seriesUsing only climate predictors, our models perform well across climate type, basin size, degree of regulation, and impervious areaSeasonal climate predictors are suitable to capture complex interplays of different climate and catchment processes leading to peak flows [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigated the effect of varying levels of dietary fat in concentrate mixes on growth performance, rumen characteristics, digestibility, blood metabolites, and methane emissions in growing Hanwoo steers. The experiment was conducted for 12 weeks by feeding three concentrate mixes with different levels of dietary fat (i.e., 48, 74, 99 g/kg dry matter [DM] of concentrate mix). Increasing fat levels in the concentrate mix significantly reduced the intake of concentrate mix (p < 0.01); however, there were no significant adverse effects on the growth rate, feed efficiency, and digestibility (p > 0.05). Increasing fat levels also affected the characteristics of rumen fluid, increasing the proportion of propionate (p < 0.01) and decreasing the proportion of butyrate and acetate to propionate in the total volatile fatty acids (p < 0.01). Cholesterol in blood serum increased significantly with increasing fat levels (p < 0.01). The highest fat level showed 18% lower methane concentration in exhaled gas from eructation than the lowest (basal) fat level (p < 0.05). In conclusion, increasing the fat level up to 100 g/kg DM in the concentrate mix reduced methane concentration in the exhaled gas from eructation without altering growth performance in growing Hanwoo steers. This study investigated the effect of different dietary fat levels in concentrate mixes on the growth performance, rumen characteristics, digestibility, blood metabolites, and methane emissions in growing Hanwoo steers. Thirty steers (386 ± 24.6 kg of body weight [BW]; 12 months old), blocked by BW, were randomly assigned to three dietary treatments with varying fat concentrations in concentrate mix (48, 74, and 99 g of ether extract per kg dry matte [DM]). The fat intake of the low-fat treatment represented 4.15% of the total dry matter intake (DMI), while the medium- and high-fat treatments accounted for 5.77% and 7.23% of total DMI, respectively. Concentrate mix DMI decreased with increasing fat level (p < 0.01). The growth rate and digestibility did not significantly differ based on the fat level (p > 0.05). As the fat level increased, propionate in the total ruminal volatile fatty acids increased, and butyrate and acetate-to-propionate decreased (p < 0.01). Cholesterol in blood serum increased significantly with increasing dietary fat levels (p < 0.01). Methane emissions exhibited a linear decrease with increasing fat level (p < 0.05). In conclusion, elevating fat content in the concentrates up to 100 g/kg DM reduced methane emissions without compromising the growth performance of growing Hanwoo steers. [ABSTRACT FROM AUTHOR]

Coastal areas are subject to the joint risk associated with rainfall‐driven flooding and storm surge hazards. To capture this dependency and the compound nature of these hazards, bivariate modelling represents a straightforward and easy‐to‐implement approach that relies on observational records. Most existing applications focus on a single tide gauge–rain gauge/streamgauge combination, limiting the applicability of bivariate modelling to develop high‐resolution space–time design events that can be used to quantify the dynamic, that is, varying in space and time, compound flood hazard in coastal basins. Moreover, there is a need to recognize that not all extreme events always come from a single population, but can reflect a mixture of different generating mechanisms. Therefore, this paper describes an empirical approach to develop design storms with high‐resolution in space and time (i.e., ~5 km and hourly) for different joint annual exceedance probabilities. We also stratify extreme rainfall and storm surge events depending on whether they were caused by tropical cyclones (TCs) or not. We find that there are significant differences between the TC and non‐TC populations, with very different dependence structures that are missed if we treat all the events as coming from a single population. While we apply this methodology to one basin near Houston, Texas, our approach is general enough to make it applicable for any coastal basin exposed to compounding flood hazards from storm surge and rainfall‐induced flooding. [ABSTRACT FROM AUTHOR]

Baseflow is an important water resource because it supplies streamflow between precipitation events and during dry seasons. We report the first continental assessment of baseflow in Africa by analyzing the baseflow index, baseflow seasonality, and monthly baseflow trends for three periods (1950–2018, 1950–1980, and 1981–2018). To explain changes in baseflow, we analyze precipitation trends. Results highlight that the baseflow season in west-central Africa occurs during August–November. In southern and northern Africa, the baseflow season is similar (January–May and January–April, respectively). Trend analysis detected monthly baseflow decreases in west-central Africa over the entire record and from 1950 to 1980, with increases after 1980. A clear pattern was absent in southern Africa for the whole record, but increases were detected earlier and decreases from 1981 to 2018. In northern Africa, decreasing baseflow trends were pronounced over the whole record, with no clear shift present. Precipitation trends were only consistent with baseflow changes in west-central Africa. [ABSTRACT FROM AUTHOR]