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The\ncomplexity of distinguishing human-induced climate signals from natural\nvariability has challenged traditional detection and attribution (D&A)\napproaches, which seek to identify specific \"fingerprints\" in climate response\nvariables. Deep learning offers potential for discerning these complex patterns\nin expansive spatial datasets. However, lack of standard protocols has hindered\nconsistent comparisons across studies. We introduce ClimDetect, a standardized\ndataset of over 816k daily climate snapshots, designed to enhance model\naccuracy in identifying climate change signals. ClimDetect integrates various\ninput and target variables used in past research, ensuring comparability and\nconsistency. We also explore the application of vision transformers (ViT) to\nclimate data, a novel and modernizing approach in this context. Our open-access\ndata and code serve as a benchmark for advancing climate science through\nimproved model evaluations. 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