Large language models (LLMs) are increasingly used as proxies for computational social analysis, yet faithfully representing the "thick descriptions" (Geertz, 1973) of human communities remains a critical challenge. Current evaluations often reduce social identity to static labels, sidelining how real-world groups navigate social shifts. To bridge this gap, we introduce CARE (Community-Aware Reaction Evaluation), a reaction-centered framework that benchmarks LLM-simulated discourse against the authentic, event-contingent responses of distinct communities to real-world news. Spanning 207 Reddit communities and covering 9,947 authentic reactions towards 2,166 news articles, CARE evaluates leading LLMs using a hierarchical taxonomy covering coarse attitudes and fine-grained communicative tones. Our empirical findings expose two critical failure modes in prevailing community-conditioning paradigms. First, while community context and targeted reasoning significantly enhance macro-level attitudinal and tonal profiling, these gains largely collapse at the instance level when predicting reactions to specific events. Second, the benefits of community conditioning are remarkably uneven: prompting strategies yield non-uniform shifts, where fidelity gains in some communities are offset by performance drops in others. This micro-macro divergence and community-level instability demonstrate that standard conditioning enables models to approximate static baseline profiles without capturing dynamic or equitable event reactions, establishing CARE as an essential diagnostic tool for community-aware social simulation. Our code and data are available at https://github.com/nuankw/Handle_with_CARE.

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