Weather extremes have widespread harmful impacts on ecosystems and human communities with more deaths and economic losses from flash floods than any other severe weather-related hazards. Flash floods attributed to storm runoff extremes are projected to become more frequent and damaging globally due to a warming climate and anthropogenic changes, but previous studies have not examined the response of these storm runoff extremes to naturally and anthropogenically driven changes in surface temperature and atmospheric moisture content. Here we show that storm runoff extremes increase in most regions at rates higher than suggested by Clausius-Clapeyron scaling, which are systematically close to or exceed those of precipitation extremes over most regions of the globe, accompanied by large spatial and decadal variability. These results suggest that current projected response of storm runoff extremes to climate and anthropogenic changes may be underestimated, posing large threats for ecosystem and community resilience under future warming conditions.

Droughts produce a complex set of negative economic, environmental, and social impacts from regional to national scales. The drought impact can be quantified using drought index time series. This study uses the monthly standardized precipitation index (SPI) time series as a drought index. Drought characteristics, namely, drought duration, severity, interval time and minimum SPI values, were determined. Five hundred years of daily rainfall data were simulated for evaluating drought characteristics. Appropriate distributions were selected for modeling drought duration, interval time, drought severity, and minimum SPI value in different drought states. The drought episodes were quantified using multivariate copula methods. Several copulas from the Archimedean and metaelliptical families were applied to construct four-dimensional joint distributions. The dependence structure in each drought state was investigated and drought probabilities and return period were calculated and analyzed based on a four-dimensional copula using the upper Han River basin, China, as a study area.

The Paris Agreement sets a long-term temperature goal to hold global warming to well below 2.0 ^∘C and strives to limit it to 1.5 ^∘C above preindustrial levels. Droughts with either intense severity or a long persistence could both lead to substantial impacts such as infrastructure failure and ecosystem vulnerability, and they are projected to occur more frequently and trigger intensified socioeconomic consequences with global warming. However, existing assessments targeting global droughts under 1.5 and 2.0 ^∘C warming levels usually neglect the multifaceted nature of droughts and might underestimate potential risks. This study, within a bivariate framework, quantifies the change in global drought conditions and corresponding socioeconomic exposures for additional 1.5 and 2.0 ^∘C warming trajectories. The drought characteristics are identified using the Standardized Precipitation Evapotranspiration Index (SPEI) combined with the run theory, with the climate scenarios projected by 13 Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models (GCMs) under three representative concentration pathways (RCP 2.6, RCP4.5 and RCP8.5). The copula functions and the most likely realization are incorporated to model the joint distribution of drought severity and duration, and changes in the bivariate return period with global warming are evaluated. Finally, the drought exposures of populations and regional gross domestic product (GDP) under different shared socioeconomic pathways (SSPs) are investigated globally. The results show that within the bivariate framework, the historical 50-year droughts may double across 58 % of global landmasses in a 1.5 ^∘C warmer world, while when the warming climbs up to 2.0 ^∘C, an additional 9 % of world landmasses would be exposed to such catastrophic drought deteriorations. More than 75 (73) countries' populations (GDP) will be completely affected by increasing drought risks under the 1.5 ^∘C warming, while an extra 0.5 ^∘C warming will further lead to an additional 17 countries suffering from a nearly unbearable situation. Our results demonstrate that limiting global warming to 1.5 ^∘C, compared with 2 ^∘C warming, can perceptibly mitigate the drought impacts over major regions of the world.

Atmospheric moisture holding capacity increases with temperature by about 7% per °C according to the Clausius‐Clapeyron relationship. Thermodynamically then, precipitation intensity should exponentially intensify and thus worsen flood conditions as the climate warms. However, regional and global analyses often report a nonmonotonic (hook) scaling of precipitation and runoff, in which extremes strengthen with rising temperature up to a maximum or peak point (T_pp) and decline thereafter. The underlying cause of this hook structure is not yet well‐understood, and whether it may shift and/or regulate storm runoff extremes under anthropogenic climate warming remains unknown. Here, we examine temperature scaling of precipitation and storm runoff extremes under different climate conditions using observations and large ensemble hydroclimatic simulations over mainland China. In situ observations suggest a spatially homogeneous, negative response of relative humidity to warming climates over 34.6% of the land area, and the remaining hook‐dominated regions usually show a colder T_pp than that of precipitation or storm runoff extremes. The precipitation and streamflow series over mainland China's catchments throughout the 21st century are projected by a model cascade chain under a high‐end emission scenario (RCP 8.5), which involves 31 CMIP5 climate models, 11 CMIP6 climate members, a daily bias correction method, and four lumped conceptual hydrological models. The CMIP5 ensemble projects that the hook structures shift toward warmer temperature bins, resulting in 10%–30% increases in storm runoff extremes over mainland China, while the CMIP6 ensemble projects more severe flood conditions in future warming climates.

The coincidence of flood flows of the mainstream and its tributaries may determine flood peaks. This study analyzed the risk of flooding as a result of such flood coincidences by considering flood magnitudes and time (dates) of occurrence. The Pearson Type III (P3) and log-Pearson Type III (LP3) distributions were selected as the marginal distribution of flood magnitude for annual maximum flood series; the mixed von Mises distribution was selected as the marginal distribution of flood occurrence dates. Two four-dimensional (4D) copula functions were developed for the joint distribution of flood magnitudes and occurrence dates. The upper Yangtze River in China and the Colorado River in the United States were selected to evaluate the method of computing risk. The coincidence probabilities of flood magnitudes and dates were calculated, and the conditional probabilities for the Three Gorges Reservoir (TGR) were analyzed. Results show that the von Mises distribution can fit the observed flood dates dat...