Time series forecasting has rapidly advanced across diverse domains, and the effective utilization of exogenous variables has become a critical factor in improving predictive performance. In air pollution forecasting, satellite-based spatiotemporal datasets such as those obtained from the Geostationary Environment Monitoring Spectrometer (GEMS) have emerged as essential sources of exogenous information. However, such satellite data are difficult to effectively integrate into time series forecasting models due to issues such as spatial mismatches between satellite grids and ground-based observations. To address this limitation, this study proposes Target Variance Minimization Clustering (TVMC) that represents GEMS spatiotemporal data as informative exogenous variables for ground-level air pollution forecasting. Specifically, a 3D Convolutional AutoEncoder (3D-CAE) is employed to encode GEMS spatiotemporal patches into latent vectors. Our proposed method constructs clusters such that the intra-cluster variance of the forecasting targets is minimized for each latent representation. We theoretically show that the clusters generated by our method exhibit greater mutual information between the cluster assignments and the target variable compared to those formed by conventional clustering methods. Experimental results show that the proposed TVMC-based representation consistently improves performance across diverse time series forecasting models, including state-of-the-art methods.