This study evaluates Vision Transformers (ViTs) for detecting anthropogenic climate change signals, crucial for effective policy planning and risk assessment. Compared to previously suggested models like CNN, MLP, and ridge regression, ViTs consistently detect forced climate signals earlier across three reanalysis datasets (ERA5, JRA-3Q, and MERRA-2). Interpretation with Integrated Gradients reveals consistent spatial patterns, suggesting ViTs utilize physically-grounded signals. This work highlights ViTs' potential to advance climate change detection and attribution tasks.