Meteorological observations are essential for climate modelling, prediction, early warning systems, decision-making processes, and disaster management. These observations are critical to societal development and the safeguarding of human activities and livelihoods. Spatial interpolation techniques play a pivotal role in addressing gaps between observation sites, enabling the generation of continuous meteorological datasets. However, due to the inherent complexity of atmosphere–surface interactions, no single interpolation technique has proven universally effective in achieving consistently accurate results for meteorological variables. This study proposes a novel interpolation model based on Fuzzy Adaptive Optimal Fusion (FAOF). The FAOF model integrates fuzzy theory by constructing station-specific fuzzy sets and sub-method element pools, employing a nonlinear membership function with error as the independent variable. An iterative accuracy index is used to identify the optimal parameter combination, facilitating adaptive data fusion and interpolation optimisation. The model’s performance is evaluated against 10 individual methods from the method pool. Experimental results demonstrate that FAOF effectively combines the strengths of multiple methods, achieving significantly enhanced interpolation accuracy. Additionally, the model consistently performs well across diverse regions and meteorological variables, underscoring its robustness and strong generalisation capability.

This article proposes an enhanced quality control (QC) method based on Spatiotemporal Graph Convolutional Networks (STGCN) to identify potential outliers in surface temperature observations. The STGCN model employs a graph structure to simultaneously capture temporal and spatial dependencies, with the adjacency matrix constructed using spatial distances and topography-assisted elevation priors. Compared to baseline methods, experimental results indicate that STGCN achieves superior overall performance across evaluation metrics, effectively balancing Type I and Type II errors. The findings demonstrate that the proposed framework is an effective QC method for detecting observational anomalies in surface temperature datasets.