In this study, we examined the summertime climatic effects of urban expansion during 1990–2010 in the Yangtze River Delta (YRD) region by analyzing station observations and performing numerical simulations with the Weather Research and Forecast (WRF) model. Long‐term observations showed that urban area experienced a larger increase in summertime 2‐m air temperature than rural part during 1980–2018, and the influence of urbanization on the urban‐rural contrast was greater in the late stage (after 2000) than the early stage (before 2000). We applied the WRF model incorporated with historical land surface cover data (year 1990, 2000, and 2010) to further evaluate the climatic effects of long‐term urbanization. On average, urban expansion over 1990–2010 led to 0.75°C increase in daily average temperature (1.06°C in daily minimum and 0.45°C in daily maximum) during the summer. The summertime daily temperature range decreased by 0.61°C in urban environment during the same period. Compared to the warming effect of urbanization in the 1990s, both the magnitude and affected area have increased after the millennium. Also, urban expansion reduced moisture in low‐level atmosphere, and this urban dry island (UDI) effect was enhanced in the late stage. Less moisture in the atmosphere offset heat stress index induced by the warming temperature. We also found that the partitioning of net radiation between sensible and latent heat was the key factor that controlled urban warming effect.

Wetland plays a pivotal role in sustaining ecosystems and adapting to climate change. This paper used remote sensing images from 1990, 2000, 2010, and 2020 to investigate the changes in wetland in the Dongting Lake Basin (DLB) and their possible causes. The land-use conversion matrix and contribution rate were calculated in 1990–2000, 2000–2010, and 2010–2020, and results showed that the total wetland area displayed an increasing trend, especially the reservoir ponds and channels across DLB from 1990 to 2020. Forest and agricultural land conversion into wetland accounted for the main proportion, with the greatest contribution rate (234.13%) of forest land and the smallest rate (−117.46%) of agricultural land between 1990 and 2000. On the contrary, agricultural land had the highest contribution rate (47.96%) for wetlands compared to other land-cover types from 2000 to 2010, followed by forest land (39.03%). The contribution rates of forest and agricultural lands to wetlands were 60.17% and 39.02% from 2010 to 2020, respectively. Wetlands showed a more significant net gain (a total of 259 km²) in Central and Southern Hunan Province. More specifically, the wetlands area in North Hunan Province decreased by 45 km² from 1990 to 2000. It increased over the next two decades (155 km² and 22 km², respectively). Southern Hunan Province continued increasing from 1990 to 2010 (a total of 149 km²) while decreasing from 2010 to 2020 (a total of −297 km²). Forestation was the principal driving force promoting the continuous increase in wetlands. In addition, agricultural land was mainly related to wetland change in this region, characterized by reclaiming land from lakes in the earlier period and returning agricultural land to wetland in the later period. Built-up land occupied a small area of wetlands over the study period. The study is beneficial to understanding the wetlands’ dynamic changes in the past and present, as well as being useful for wetland management, consistent with sustainable development.

Understanding extreme precipitation (EP) evolution is crucial for global climate adaptation and hazardous disasters prevention. However, spatial non-stationarity of urbanization relationships with EP variations has been rarely discussed in a complex topographic context. Taking the city Liuzhou in China as the example, this study separately quantified the evolution of EP intensity, magnitude, duration, and frequency on different temporal scales with Innovative Trend Analysis (ITA). Based on a finer spatial (5 km grid) scale and multiple temporal (daily, daytime, nighttime, and 14 h) scale analyses, it innovatively identified spatially varying urbanization effects on EP with more details in different elevations. Our results indicate that: (1) from 2009 to 2023, EP events became more intense, persistent, and frequent, particularly for higher-grade EPs and in the steeper north of Liuzhou; (2) despite the globally negative correlations, spatial correlations between comprehensive urbanization (CUB) and each EP index on individual temporal scales were still explicitly categorized into four types using LISA maps—high-high, high-low, low-low, and low-high; (3) Geographically Weighted Regression (GWR) was demonstrated to precisely explain the response of most EP characteristics to multiple manifestation of urbanization with respect to population (POP), economy (GDP), and urban area (URP) expansion (adjusted R²: 0.5–0.8). The predictive accuracy of GWR on urbanization and EPs was spatially non-stationary and variable with temporal scales. The local influential strength and direction varied significantly with elevations. The most significant and positive influences of three urbanization predictors on EPs occurred at different elevation grades, respectively. Compared with POP and GDP, urban area percent (URP) was indicated to positively relate to EP changes in more areas of Liuzhou. The spatial and quantitative relationships between urbanization and EPs can help to guide effective urban planning and location-specific management of flood risks.