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The abrupt alternation between hot and wet extremes can lead to more severe societal impacts than isolated extremes. However, despite an understanding of hot and wet extremes separately, their temporally compounding characteristics are not well examined yet. Our study presents a comprehensive assessment of successive heat-pluvial and pluvial-heat events globally. We find that these successive extremes within a week occur every 6–7 years on average within warm seasons during 1956–2015, about 15% more often than would be expected by chance, and that they have a significant increase in frequency of about 22% per decade due to warming. We further investigate the role of vapor pressure deficit (VPD) and find that heat-pluvial (pluvial-heat) events are linked to negative (positive) VPD anomalies. Our results are statistically significant based on moving-blocks bootstrap resampling and field significance tests, highlighting these methods' importance in robustly identifying compound events under autocorrelation and multiple-testing conditions.

The emergence of abrupt shift from drought to downpour has attracted widespread attention in recent years, with particularly disastrous consequences in low‐income regions. However, the spatiotemporal evolution and poverty exposure to such drought‐to‐downpour events remain poorly understood. Here, we investigate the connection between poverty and drought‐to‐downpour events based on three data products and climate models on a global scale. We find that the drought‐to‐downpour events increased by 24%–48% in the poorest 20% of the world's population from 1980 to 2010. The drought‐to‐downpour events do not appear to be occurring more frequently in most regions globally, just affecting regions with higher poverty rates more frequently, especially in African countries. The exposure inequality remains under future socioeconomic pathways, with a nearly fivefold increase in the exposure for the poorer populations. Poverty exposure to more frequent drought‐to‐downpour events demands greater support for climate adaptation in low‐income countries to reduce poverty and inequality. Plain Language Summary: Many regions have suffered greatly from recent occurrences of abrupt shift from drought to downpour, suggesting that the emerging threat is a global challenge. Such drought‐to‐downpour events pose challenges to water infrastructures in developed countries, let alone those poor countries with limited adaptation capacity and resources. However, the connection between the drought‐to‐downpour events and poverty incidence remains poorly understood. Here, we show that such drought‐to‐downpour events experienced by the poorest 20% of the world's population increased significantly by 24%–48% from 1980 to 2010. Such a significant increase, however, is not observed for the remaining wealthiest 80%. The drought‐to‐downpour events do not appear to be occurring more frequently in most global regions, just affecting regions with higher poverty rates more frequently, especially in African countries. Climate projections show that such inequality would remain in a warming climate. Our results highlight the urgency to provide greater support for climate adaptation in low‐income countries to reduce poverty and inequality. Key Points: The connection between poverty incidence and drought‐to‐downpour weather whiplashes is uncovered on a global scaleThe drought‐to‐downpour events increased by 24%–48% in the poorest 20% of the world's population from 1980 to 2010The drought‐to‐downpour events do not appear to be occurring more frequently in most global regions, just affecting regions with higher poverty rates more frequently [ABSTRACT FROM AUTHOR]

Seawater inundation mapping plays a crucial role in climate change adaptation and flooding risk reduction for coastal low-lying areas. This study presents a new elevation model called the digital impermeable surface model (DISM) based on the topographical data acquired by unmanned aerial vehicle (UAVs) for improving seawater inundation mapping. The proposed DISM model, along with the bathtub model, was used to assess coastal vulnerability to flooding in significant tropical cyclone events in a low-lying region of Victoria Harbor in Hong Kong. The inundation simulations were evaluated based on the typhoon news and reports which indicated the actual storm surge flooding conditions. Our findings revealed that the proposed DISM obtains a higher accuracy than the existing digital elevation model (DEM) and the digital surface model (DSM) with a RMSE of 0.035 m. The DISM demonstrated a higher skill than the DEM and the DSM by better accounting for the water-repellent functionality of each geospatial feature and the water inflow under real-life conditions. The inundation simulations affirmed that at least 88.3% of the inundated areas could be recognized successfully in this newly-designed model. Our findings also revealed that accelerating sea level rise in Victoria Harbor may pose a flooding threat comparable to those induced by super typhoons by the end of the 21st century under two representative emission scenarios (RCP4.5 and RCP8.5). The seawater may overtop the existing protective measures and facilities, making it susceptible to flood-related hazards. [ABSTRACT FROM AUTHOR]

The scale effects of the spatial autocorrelation (SA) measurement has been explored for decades. However, the effects of the data aggregation levels and spatial resolution on the SA measurement are often confused. Whether the two types of scale effects are the same is still unclear and requires further investigation. We retrieved the land surface temperature (LST) from Landsat 8 images in 30 capital cities of China. By aggregating the LST images, we observed a decrease in the SA of the LST as the data aggregation level increased; this relationship can be fitted well with a negative logarithmic function. We derived an indicator to measure the scale effects intensity of SA, which was negatively correlated with the spatial complexity of LST. Both aggregating images and the increasing spatial resolution induce weaker SA, but the effect of the former was stronger. The aggregating images negatively affected the SA degree regardless of the spatial resolutions of the original images. The SA degrees of the aggregated images were far below those of the real-life images. This study suggests that the scale effects caused by aggregation levels and spatial resolutions are different, and cautions should be taken when applying relevant conclusions derived from aggregating images. [ABSTRACT FROM AUTHOR]

Though it is recognized that meteorology has a great impact on the diffusion, accumulation and transport of air pollutants, few studies have investigated the impacts on different-sized particulate matter concentrations. We conducted a systematic comparative analysis and used the framework of generalized additive models (GAMs) to explore the influences of critical meteorological parameters, wind and precipitation, on PM2.5, PM10 and PM2.5-10 concentrations in Wuhan during 2013-2016. Overall, results showed that the impacts of wind and precipitation on different-sized PM concentrations are significantly different. The fine PM concentrations decreased gradually with the increase of wind speed, while coarse PM concentrations would increase due to dust resuspension under strong wind. Wind direction exerts limited influence on coarse PM concentrations. Wind speed was linearly correlated with log-transformed PM2.5 concentrations, but nonlinearly correlated with log-transformed PM10 and PM2.5-10 concentrations. We also found the PM2.5 and PM2.5-10 concentrations decreased by nearly 60 and 15% when the wind speed was up to 6 m/s, respectively, indicating a stronger negative impact of wind-speed on fine PM than coarse PM. The scavenging efficiency of precipitation on PM2.5-10 was over twice as high as on PM2.5. Our findings may help to understand the impacts of meteorology on different PM concentrations as well as discriminate and forecast variation in particulate matter concentrations. [ABSTRACT FROM AUTHOR]