Combating climate change by increasing urban carbon storage is one of the critical issues which urban policymakers must address. Understanding the characteristics and driving factors of carbon storage changes during urbanization can assist urban managers in formulating responsive land use policies. This study employs the INVEST model to evaluate carbon storage in Shanghai from 2000 to 2020, analyzing land use changes and their carbon impacts. It analyzes the transformation of land use in Shanghai during the same period and its impact on carbon storage. Using a 1 km grid for sampling, this study examines the spatiotemporal distribution patterns of carbon storage in Shanghai. Furthermore, it employs linear regression to discuss the social and economic drivers influencing carbon storage in the city. Carbon storage in Shanghai, predominantly from cultivated land and artificial surfaces, increased from 16.78 Mt in 2000 to 18.40 Mt in 2020, with an annual rise of 0.81 Mt. The spatial distribution of carbon storage exhibited a stable southeast-northwest pattern, with variations in dispersion between the north-south and east-west directions. The distribution of carbon storage shifted from a bimodal to a unimodal pattern, indicating an overall increase. There was a significant positive correlation between carbon storage and both the per capita green space area and the industrial output value, which can be attributed to Shanghai’s policies on green industrial development. This research aids in formulating land use policies to enhance urban carbon storage.

Under the context of rapid urbanization and climate change, urban ecosystem services (ES) have undergone dramatic transformations. Elucidating the trade-off and synergy relationships among ES and quantifying how urbanization mediates these relationships are critical to achieving urban sustainability. Focusing on Shanghai during 2000–2020, we quantified three climate-related ES—water yield (WY), urban cooling (Heat Mitigation Index, HMI) and carbon storage (CS)—with the InVEST model. We then examined the spatio-temporal evolution of these services, analyzed their trade-offs and synergies, and examined the underlying urbanization drivers. Results show that total WY increased by 76%, with peak volumes concentrated in the central districts; HMI declined, with low-value zones spreading inward; CS rose and became spatially more homogeneous. WY–HMI trade-offs intensified, whereas CS–HMI were synergistic (r = 0.33–0.61) except in core districts where built-up expansion created trade-offs. CS–WY trade-offs weakened, becoming synergistic in most districts by 2020. HMI loss was driven by GDP and industrial output (p < 0.05). Per-capita green-space area was positively correlated with HMI but exerted no significant influence on CS or WY, highlighting the limitations of ecological interventions focused on single ES enhancement.

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting.

Urban riparian areas serve as vital blue-green infrastructure for climate adaptation, yet mechanisms governing energy exchange remain underexplored. This study aims to quantify the spatiotemporal patterns of sensible heat flux (H) and latent heat flux (LE) across riparian plant communities on daily and annual scales, and to disentangle the interactive effects of vegetation structure and water bodies on these fluxes. Using year-long field monitoring (September 2020–August 2021) across seven riparian plant communities along the Danshui River in Shanghai, environmental parameters were collected at multiple distances from the river. Interpretable machine learning models (Random Forest with SHAP analysis) were employed to identify key drivers. Results reveal significant diurnal and seasonal dynamics: LE amplitude exceeded H in summer but reversed in winter, with spatial gradients in H and LE strongly influenced by proximity to water bodies in grasslands and broadleaf forests but weakened in conifers. Meteorological factors such as photosynthetically active radiation and sunshine duration dominated daily-scale fluxes, while vegetation structures such as canopy height and leaf area index (LAI) contributed >50% to annual-scale variability. These findings underscore vegetation’s role in modulating energy partitioning, providing a theoretical basis for optimizing riparian plant configurations to enhance microclimate regulation in urban riparian.