Dataset distillation (DD) excels in synthesizing a small number of images per class (IPC) but struggles to maintain its effectiveness in high-IPC settings.Recent works on dataset distillation demonstrate that combining distilled and real data can mitigate the effectiveness decay. However, our analysis of the combination paradigm reveals that the current one-shot and independent selection mechanism induces an incompatibility issue between distilled and real images. To address this issue, we introduce a novel curriculum coarse-to-fine selection (CCFS) method for efficient high-IPC dataset distillation.CCFS employs a curriculum selection framework for real data selection, where we leverage a coarse-to-fine strategy to select appropriate real data based on the current synthetic dataset in each curriculum.Extensive experiments demonstrate the effectiveness of CCFS, achieving significant improvements over the state-of-the-art: +6.6\% on CIFAR-10, +5.8\% on CIFAR-100, and +3.4\% on Tiny-ImageNet in high-IPC settings. Notably, we achieve 60.2\% test accuracy on ResNet-18 with a 20\% compression ratio of Tiny-ImageNet, yielding similar performance as full dataset training with only 0.3\% performance degradation.