Text-to-image models can render recognizable cultural symbols, but cultural activity generation requires coordinated control over participants, actions, interactions, scenes, and culture-specific constraints. We propose CARE, an evidence-grounded visual checklist framework for controllable cultural activity image generation. Given an activity prompt, CARE builds source-traceable cultural evidence, converts it into an optimized prompt and fine-grained checklist, verifies generated images with concrete VLM-based visual checks, and uses failed checks for targeted prompt repair. We introduce CARE-1360, a dataset of 1,360 cultural activity prompts across eight countries, and evaluate generated images with a unified human protocol. Experiments show that CARE consistently improves over direct generation and retrieval-based prompt refinement baselines across cultural and visual quality dimensions. Results with both ZImage and Stable Diffusion XL suggest that evidence-grounded visual checklists provide an interpretable control mechanism that transfers across the tested generation backends.