Global warming is increasing the frequency and intensity of extreme weather events around the world. The extreme climate in plateau and mountainous areas is sensitive and fragile. Based on the software Rclimdex 1.0, the spatio-temporal variation characteristics of 27 extreme climate indices at 120 meteorological stations were calculated in Yunnan from 1960 to 2019. The results show that the extreme temperature is rising, and the warming rate at night is higher than that in the daytime. It showed a trend of warming and drying, and precipitation was concentrated into more intense bursts. Extreme temperature cold indices (TX10p, TN10p, FD0, ID0, and CSDI) were negatively correlated with extreme precipitation indices (R × 5 day, PRCPTOT, R10 mm, R20 mm, and R25 mm). Extreme temperature warmth indices (TX90p and TN90p) were positively correlated with extreme precipitation indices (R × 5 day, CWD, PRCPTOT, R10 mm, R20 mm, and R25 mm). The change rate of extreme temperature does not increase linearly with altitude. The increase in middle-altitude and high-altitude areas is higher than that in low-altitude areas. Compared with ENSO and AO, NAO is a vital circulation pattern affecting the extreme climate in Yunnan. The influence of NAO on Yunnan’s extreme climate indices is most significant in the current month and the second month that follows. NAO was negatively correlated with extreme temperature warm indices (TN90p, TX90p, SU25, and TR20). NAO positively correlates with the extreme cold temperature indices (TN10p and TX10p). Except that ENSO has a significant effect on CDD, the effect of the general circulation patterns on the extreme temperature indices was more significant than that on the extreme precipitation indices in Yunnan. The results of this study are helpful to further understand and predict the characteristics of extreme climatic events and the factors affecting their geographical locations and atmospheric circulation patterns in Yunnan.

Assessing the impacts of climate change on geographic distribution by identifying how biological response relates to environmental conditions is important for addressing the adverse effects of climate change. Using the maximum entropy algorithm and spatial analysis module of ArcGIS, we construct a habitat prediction model for the geographic distribution of alpine vegetation in the Hengduan Mountains of southwestern China. We use the model to identify how alpine vegetation has responded to climatic changes during the period 1980–2018, and to predict responses to possible temperature and precipitation changes in the future. The results indicate that the geographic distribution of alpine vegetation in the Hengduan Mountains is most sensitive to annual mean minimum temperature variation. The most suitable habitat for alpine vegetation under climate change is in northernmost Sichuan, in the Hengduan–Himalayan mountain area, at elevations from 4500 to 5000 masl. The current area of alpine vegetation in the Hengduan Mountains accounts for 8.20% of their total area. As the annual mean temperature increases, the area suitable for alpine vegetation increases by 0.32–1.27%, regardless of changes in precipitation. When the temperature increases by 1–2°C, the area unsuitable for alpine vegetation decreases by 1.64–2.97%. These results indicate that a temperature increase influences the geographic distribution pattern of alpine vegetation in the Hengduan Mountains. The most notable effect was under the 2°C increase scenario. The predictions suggest that under future climate change, the geographic distribution of alpine vegetation will continue to shift toward middle and higher elevations. To protect endemic alpine vegetation, the best habitats should be protected from interference and destruction by human activities.

The acceleration of global warming leads to frequent extreme weather events. The sensitivity of high‐elevation regions to global warming makes their extremes more prominent. Based on the software Rclimdex1.0, 15 extreme climate indices from 120 meteorological stations in Yunnan from 1960 to 2019 were calculated. Trend analysis, correlation analysis, and cross‐correlation analysis were used to investigate the spatiotemporal variation characteristics of these extreme climate indices. The results showed that, except CDD, other extreme precipitation indices were negatively correlated with the increase of elevation. The extreme warming indices (SU25, WSDI, TX90p, TN90p) showed an increasing trend with the increase of elevation, while the extreme cold indices (CSDI, FD0, TX10p, TN10p) showed a decreasing trend with the increase of elevation. The rate of nocturnal warming at high elevations is higher than that at low elevations. The change rate of extreme warmth indices (SU25, WSDI, TX90p, TN90p) and extreme cold indices (FD0, CSDI, TX10p, TN10p) was negatively correlated (p < .05) in all elevation ranges, and this correlation was most significant in the elevation range of 1,500–2,000 m. These results highlight that the rate of drying and warming at higher elevations is higher than that at lower elevations, providing case support and scientific basis for sensitivity of high‐altitude regions to extreme climate.

Extreme drought has negative impacts on the health of vegetation and the stability of ecosystems. In this study, the CASA model was employed to estimate the net primary productivity of vegetation over the Yunnan Plateau. The time-lag effects on vegetation were observed within a 0–6 month period of extreme droughts using the Pearson correlation coefficient. The resistance of vegetation during extreme droughts was quantified, and the recovery capability of vegetation following these events was analyzed using the ARIMA model. Moreover, the study investigated the response of vegetation to extreme droughts across diverse altitudinal gradients. The results showed that: (1) This round of extreme drought led to a decrease in the NPP of vegetation in the Yunnan Plateau. (2) Vegetation exhibits a 1–3-month lag period in response to extreme drought, with forests showing slower responses than grasslands and shrubs and higher resistance to the drought. Except for agricultural vegetation, most other vegetation types are able to recover their productivity within a year. (3) Vegetation above 3000 m is less susceptible to the impacts of extreme drought. With increasing elevation, forests exhibit an earlier lag period in response to extreme drought and an increase in resistance, but lower elevation vegetation demonstrates better recovery from extreme drought events. Shrub vegetation shows the highest resistance at elevations between 3000–4000 m, and shrubs at middle to high elevations have better recovery capacity than those at low elevations. Grassland vegetation exhibits increased resistance to extreme drought with higher elevation and shows better recovery. Agricultural vegetation demonstrates higher resistance at middle to high elevations, with no significant elevation differences in recovery capacity. Extreme drought events not only have a lag effect on the vegetation ecosystem, but also affect its stability and resilience to future drought events. To adapt to climate change, future research should emphasize the role of small-scale climate in vegetation’s response to drought.

The urban heat island effect (UHI) has become a major challenge for sustainable urban development. In recent decades, the significant development of urban agglomerations has intensified the complex interaction and comprehensive impact of the UHI effect, but the spatiotemporal pattern of regional heat islands has been poorly understood. Based on the land surface temperature (LST) from 2001 to 2020, this study uses the relative land surface temperature (RLST) method to quantify the regional heat island (RHI) of the Central Yunnan Urban Agglomeration (CYUA) beyond a single city, combines a variety of spatial analysis tools to identify the multi-scale spatiotemporal pattern, and explores the multidimensional driving factors of RHIs. The combined effects of indicators such as urbanization intensity, blue–green space intensity (2D), and building height characteristics (3D) on the mitigation or exacerbation of RHIs are included. The results are as follows: (1) The RHI was significantly enhanced, especially during 2011–2014, when the heat island intensity and influence range expanded rapidly, especially in the core areas such as Kunming and Qujing. (2) The main urban areas of prefecture-level cities have a greater contribution to the RHI, and the intercity heat interaction further intensifies the heat island effect on county-level regions. (3) Different land cover types have different effects on RHI. The human and social factors have a positive effect on the RHI, the blue–green intensity has a strong inhibitory effect, and the cooling effect of blue space is better than that of green space. Topographic and meteorological factors have little influence. To effectively address the challenge of UHI, the CYUA must strengthen the construction of green infrastructure, optimize urban planning, promote energy conservation and emission reduction, and improve climate adaptation planning. This paper discusses the spatiotemporal variation in the heat island effect and the influencing factors from a new regional perspective, which enriches the research content of urban agglomeration thermal environment and improves the research system of the heat island effect.

The relationship between biodiversity and ecosystem functioning is an important issue in ecology. Plant functional traits and their diversity are key determinants of ecosystem function in changing environments. Understanding the successional dynamics of functional features in forest ecosystems is a first step to their sustainable management. In this study, we tested the changes in functional community composition with succession in tropical monsoon forests in Xishuangbanna, China. We sampled 33 plots at three successional stages—~40-year-old secondary forests, ~60-year-old secondary forests, and old growth forests—following the abandonment of the shifting cultivation land. Community-level functional traits were calculated based on measurements of nine functional traits for 135 woody plant species. The results show that the community structures and species composition of the old-growth forests were significantly different to those of the secondary stands. The species diversity, including species richness (S), the Shannon–Weaver index (H), and Pielou’s evenness (J), significantly increased during the recovery process after shifting cultivation. The seven studied leaf functional traits (deciduousness, specific leaf area, leaf dry matter content, leaf nitrogen content, leaf phosphorus content, leaf potassium content and leaf carbon content) changed from conservative to acquisitive syndromes during the recovery process, whereas wood density showed the opposite pattern, and seed mass showed no significant change, suggesting that leaf traits are more sensitive to environmental changes than wood or seed traits. The functional richness increased during the recovery process, whereas the functional evenness and divergence had the highest values in the 60-year-old secondary communities. Soil nutrients significantly influenced functional traits, but their effects on functional diversity were less obvious during the secondary succession after shifting cultivation. Our study indicates that the recovery of tropical monsoon forests is rather slow; secondary stands recover far less than the old growth stands in terms of community structure and species and functional diversity, even after about half a century of recovery, highlighting the importance of the conservation of old growth tropical monsoon forest ecosystems.