Understanding of future changes in drought characteristics is crucial for climate change adaptation and drought impact mitigation. We analyzed the projected changes in drought characteristics relative to historical drought conditions in Central Asia using the Standardized Precipitation Evapotranspiration Index (SPEI) based on both gridded observations and an ensemble of bias-corrected and spatially-disaggregated Global Circulation Models (GCMs) from phase 6 of Coupled Model Intercomparison Project (CMIP6). The results suggest that precipitation and potential evapotranspiration are projected to increase across Central Asia. Even though the change in wetness may not be significant and robust, a ‘dry gets drier and wet gets wetter’ pattern may emerge in future under different scenarios. Drought events in Central Asia's semi-arid and arid regions (aridity index < 0.5) are projected to become more frequent (>125%), with longer duration (>55%), higher severity (>74%) and intensity (>8%) by the end of the 21^st century under four Shared Socioeconomic Pathway (SSP) scenarios. In the coming period of 2021-2050, Central Asia is expected to have more drought events with a longer duration but lower intensity. Comparisons between different SSP scenarios stress the importance of climate change mitigation strategies to avoid more drought occurrence, longer drought duration and higher drought severity. The long-term mitigation and adaptation studies for increasing drought impacts are also imperative in Central Asia.

The greening of land plays a meaningful role in improving human settlements by regulating ecosystem functions in the central coast region of China. However, research on the spatiotemporal heterogeneity of green land changes in different urbanized regions and the cooling temperature effect of the different green land densities are still lacking in this region, which limits the understanding of the effect of greening of land on land thermal properties. To address this issue, we integrated several approaches to establish a comprehensive way of ‘human–computer interactive interpretation method—urban interior mixed pixel model—surface radiation energy balance model’ using data from remote sensing images and the national land use/cover database of China, focusing on Rizhao city. The conclusions are as follows: The total greening of land from 2000 to 2022 was monitored, and it was found that its cover improved within the built-up area of the city, with the proportion of green land increasing from 25.34% in 2000 to 42.98% in 2022. Differences in the amount of green spaces in different urbanized regions were first observed, namely, the urban greening rate was 37.78% in the old urban area in 2022, while it was as high as 46.43% in the newly expanded urban area in 2022, showing that more attention should be given to the construction of urban green spaces during urban expansion. Thermal comfort indicators in the study area were evaluated in terms of latent heat flux (0–457.83 W/m²), sensible heat flux (0–645.09 W/m²), and total available energy (254.07–659.42 W/m²). We also found that the cooling temperature effect in the middle- and high-density green land regions were 1.05 °C and 2.12 °C higher than those in the low-density region, and the established comfort/discomfort zones in terms of land surface temperature were depicted. These results provide a new practical reference for exploring the spatiotemporal heterogeneity change in green land and its impact on land-surface thermal properties in coastal regions.

The central coast is one of the importantly livable regions in China, and green land plays a meaningful role in improving human settlements through regulating ecosystem services. However, the research on spatiotemporal heterogeneity of green land in different urbanized areas are still lacking in this region, which limits the timeliness of reporting on the land-surface thermal properties of residential environment from green land perspective. To address this issue, the synergistic methodology of “artificial numerical technology -- urban interior mixed pixel classification -- surface radiation energy balance model” was applied to investigate the green land change and its impact on human settlements in Rizhao region, which is known for its coastal ecology and greenery in China and obtained the United Nations habitat award. Main conclusions of this study are below: (1) Although total green land decreased by 474.05 km2, resulting in a decrease of 9.17% in its share of the entire study area from 2000-2022 during the urbanization process, but the greening levels improved within the built-up area, with the greening proportion of 25.34% in 2000 and 42.98 km2 in 2022. (2) Differences in green land at different urbanized regions were first observed, namely, urban greening rate was 37.78% in the old urban areas of 2022, while it was up to 46.43% in new urban areas. More attention was paid to the construction of urban green space during urban expansion, which will inevitably bring better visual and comfort experience to residents. (3) Thermal comfort indicators for urban residents were evaluated in latent heat flux (0-457.83 W/m2), sensible heat flux (0-645.09 W/m2), total available energy (254.07-659.42 W/m2) and others. (4) The lowered cooling temperature effect in the middle, high green land density region was 1.05 ℃ and 2.12 ℃ compared to the low-density region, and the average air temperature was 25.86 ℃. Also, spatial patterns of hot and cold uncomfortable areas were displayed in this study. Overall, this study provides the meaningfully practical reference for exploring the spatiotemporal heterogeneity of green land and its impact on residents' environments from the perspective of land-surface thermal properties in coastal areas of China and around the world.

Under the background of the global “polycrisis”, enhancing ecological resilience has become a key pathway for achieving sustainable urban development, particularly in ecologically fragile regions. This study takes the Loess Plateau as the study area and constructs an evaluation framework for urban ecological resilience based on three dimensions: resistance, adaptability, and recovery. By integrating multi-source data from 1990 to 2020, the spatiotemporal evolution of urban ecological resilience in the region is analyzed, and the Geodetector is employed to identify the impacts of natural, economic, and social factors on urban ecological resilience. The main findings are as follows: (1) The urban ecological resilience index on the Loess Plateau shows an overall trend of initial increase followed by decline. Cities in both the western and eastern parts exhibit similar evolutionary patterns, while cities in the central region demonstrate a continuous upward trend, with the resilience index increasing from 0.3658 in 1990 to 0.3838 in 2020. (2) Urban ecological resilience in the study area exhibits a spatial gradient pattern characterized by a decrease from core cities toward peripheral areas. Resilience levels gradually weaken outward from urban centers. The spatial agglomeration effect is relatively weak, with most regions showing insignificant clustering and lacking contiguous high-resilience clusters. (3) Urban ecological resilience on the Loess Plateau is jointly influenced by multiple factors, with the effects of key drivers displaying clear stage-specific characteristics. Socioeconomic factors—particularly population density, per capita GDP, and industrial structure—serve as the primary driving forces, while natural conditions provide basic constraints. Interactions among factors are dominated by bivariate interactions and nonlinear synergistic effects. These findings deepen the understanding of the evolution and driving mechanisms of urban ecological resilience in ecologically fragile regions and provide scientific support for enhancing urban resilience and promoting sustainable development in the Loess Plateau.