We present a new deep learning framework designed to assess seasonal climate predictability by identifying the key predictors that influence climate variability across different regions. This tool enhances understanding of how remote areas are connected through climate interactions and providing accurate and explainable seasonal predictions. Our results demonstrate its potential to support more reliable and informed climate services at both regional and global scales.