Amid accelerating global climate change, research quantifying the uncertainty of mountain ecosystems in relation to CMIP6 multi-model ensemble (MME) simulations remains limited. This study addresses this gap by evaluating future temperature and precipitation trends in the Hengduan Mountains and quantifying the uncertainty associated with CMIP6 MME outputs. Utilizing data from 11 CMIP6 climate models, bilinear interpolation was employed to standardize model resolution, while inverse distance weighting (IDW) interpolation was applied to assess spatial distribution patterns. To mitigate systematic biases, the multi-model ensemble mean approach was adopted. Through an equal-weight model selection strategy, EC-Earth3-Veg and MPI-ESM1-2-HR were identified as the optimal model combination for the region. Key findings include the following: (1) During the reference period (1985–2014), model simulations exhibited systematic biases, with temperatures underestimated by 0.46 ± 0.08 °C/month and precipitation overestimated by 2.07 ± 0.32 mm/month relative to observations. (2) In the future period (2031–2070), projected regional warming rates in typical years under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios are −0.294 ± 0.021 °C/decade, 0.081 ± 0.009 °C/decade, and 0.171 ± 0.012 °C/decade, respectively. (3) Precipitation is projected to decline overall, with the most pronounced decrease under the SSP5-8.5 scenario (−0.68 ± 0.07%). This study is the first to systematically quantify CMIP6 model uncertainty in the Hengduan Mountains, revealing regional climate change trajectories, providing a scientific basis for formulating adaptive strategies, and identifying critical pathways for enhancing regional climate modeling efforts.

The ongoing advancement of urbanization has significantly amplified its impacts on the water environment. Understanding the coupling relationships between urbanization and the water environment (UAWE) is crucial for Chinese policymakers aiming to promote sustainable urban development. In this study, a comprehensive UAWE evaluation model was developed to examine the development trajectories in Neijiang City from 2012 to 2022. Methodologically, a comprehensive evaluation approach was applied to assess urbanization and water resource trends over this period, followed by the development of a Coupling Coordination Degree Model (CCDM) to quantify their synergistic relationship. The results showed that the coupling between the comprehensive urbanization index and the water environment system evolved over time, as reflected in the following key findings: (1) Neijiang underwent three distinct stages from 2012 to 2022 in terms of coupling and coordination between urbanization and the water environment: Basic Coordination (2012–2015), Good Coordination (2016–2020), and Excellent Coordination (2020–2022). (2) Urbanization exerted varying impacts on subsystems of the water environment, with the pressure-response subsystems exhibiting marked volatility from 2012 to 2022. The impact intensity followed the order spatial urbanization > economic urbanization > social urbanization > population urbanization. These findings offer valuable theoretical and practical insights for aligning urban sustainability goals with effective water environment protection measures. This study provides essential guidance for policymakers in Neijiang and similar regions, enabling the development of tailored strategies for sustainable urbanization and enhanced water management.

In the context of global climate change, there is a lack of research quantifying the uncertainty of mountain ecosystems in relation to the CMIP6 multi-model ensemble simulations. This study aims to assess the future temperature and precipitation trends in the Hengduan Mountains under global climate change and quantify the uncertainty of CMIP6 multi-model ensemble simulations. Based on data from 11 CMIP6 climate models, the study uses bilinear interpolation to standardize model resolution, employs inverse distance weighting interpolation to analyze spatial distribution characteristics, and applies the multi-model ensemble mean method to reduce systematic biases. Through an equal-weight model selection method, EC-Earth3-Veg and MPI-ESM1-2-HR were identified as the optimal model combination. The research findings indicate: (1) During the reference period (1985–2014), the model simulations exhibit systematic biases, with simulated temperature values being 0.46 ± 0.08 °C/month lower and simulated precipitation values 2.07 ± 0.32 mm/month higher than the observations. (2) For the future period (2031–2070), the projected regional warming rates in typical years under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios are −0.294 ± 0.021°C/decade, 0.081 ± 0.009 °C/decade, and 0.171 ± 0.012 °C/decade, respectively. (3) Precipitation exhibits an overall decreasing trend, with the most pronounced decline under the SSP5-8.5 scenario (−0.68 ± 0.07%). This study is the first to systematically quantify the uncertainty of CMIP6 models in the Hengduan Mountains, revealing the regional climate change trends, provides scientific basis for developing adaptive strategies, and identifies key paths for improving regional climate models.