Large language models (LLMs) have shown strong performance on medical question answering benchmarks, yet existing evaluations remain limited in assessing longitudinal clinical reasoning across multiple hospital admissions. We introduce ChronoClinQA, a large-scale benchmark for longitudinal clinical reasoning grounded in multi-admission electronic health records. Built from longitudinal discharge summaries in MIMIC-IV-Note, ChronoClinQA contains 15,067 QA pairs spanning six clinically grounded reasoning dimensions and three complementary question formats: open-ended, adversarial, and unanswerable multiple-choice questions. Each instance requires reasoning over temporally accumulated patient trajectories, with contexts averaging over 16K tokens across up to eight hospital admissions. Experiments on both general-purpose and medical LLMs reveal substantial performance degradation in longitudinal and adversarial settings, highlighting persistent challenges in integrating cross-admission evidence and in temporal clinical reasoning. Our findings position ChronoClinQA as a challenging benchmark for advancing clinically grounded long-context reasoning in medical LLMs.