Scientific fact-checking is crucial for ensuring the accuracy, reliability, and trustworthiness of scientific claims. However, existing benchmarks are limited in terms of their claim diversity, reliance on text-based evidence, and oversimplification of scientific reasoning. To address these gaps, we introduce SCITAB, a novel dataset comprising 1,225 challenging scientific claims requiring compositional reasoning with scientific tables. The claims in SCITAB are derived from the actual scientific statements, and the evidence is presented as tables, closely mirroring real-world fact-checking scenarios. We establish benchmarks on SCITAB using state-of-the-art models, revealing its inherent difficulty and highlighting limitations in existing prompting methods. Our error analysis identifies unique challenges, including ambiguous expressions and irrelevant claims, suggesting future research directions. The code and the data are publicly available at https://github.com/XinyuanLu00/SciTab.

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