The increasing frequency of extreme weather events presents serious challenges to both regional and global economies. This study focuses on quantifying the economic losses caused by three major types of extreme climate events (heatwaves, droughts, and heavy rain) in Hainan Island from 2001 to 2020. Moreover, a comprehensive dataset of related economic losses was developed. To support the analysis, we constructed an Extreme Climate Economic Loss Model (ECELM). Drought and heavy rain losses were estimated using a loss intensity index based on precipitation and typhoon landfall wind speeds. Heatwave-related losses were assessed through a threshold-based optimization approach. The results show that both heatwaves and heavy rain have exhibited increasing impacts from 2001 to 2020. Although heatwaves were the most frequent extreme event in more than half of the years, heavy rain caused the highest cumulative losses, reaching CNY 75 billion. Spatial analysis indicates that the southeastern coastal areas were the most severely affected. These findings provide valuable quantitative evidence for designing targeted regional climate adaptation strategies.

Ecosystem water use efficiency (WUE) is a key indicator of the coupling between carbon and water cycles. With the increasing frequency of extreme climate events, WUE may also show trends of extremization. Understanding the dominant drivers behind extreme WUE variations is crucial for assessing the impact of climate variability on WUE. We investigate the main drivers and regional sensitivity of extreme WUE variations across seven geographical regions in China. The results reveal that extreme WUE variations are collectively influenced by gross primary productivity (GPP) and evapotranspiration (ET) (43.72%). GPP controls extreme WUE variations in 36.00% of the areas, while ET controls 20.17%. Furthermore, as the climate shifts from arid to humid regions, the area where GPP dominates extreme WUE variations increases, while the area dominated by ET decreases, suggesting a relationship with precipitation. Ridge regression analysis shows that vapor pressure deficit (VPD) is the primary driver of interannual WUE variation in China, with an average relative contribution of 38.64% and an absolute contribution of 0.025 gC·m⁻²·mm⁻¹·a⁻¹. We studied the changes in WUE and its driving mechanisms during extreme disaster events, providing a perspective focused on extreme conditions. In the future, these results may help regulate the carbon–water cycle in different regions, such as by guiding vegetation planting and land use planning based on the spatial characteristics of the dominant factors influencing extreme WUE variations to improve vegetation WUE.