Convolutional neural networks (CNN) have achieved the top performance for event detection due to their capacity to induce the underlying structures of the $k$-grams in the sentences. However, the current CNN-based event detectors only model the consecutive $k$-grams and ignore the non-consecutive $k$-grams that might involve important structures for event detection. In this work, we propose to improve the current CNN models for ED by introducing the non-consecutive convolution. Our systematic evaluation on both the general setting and the domain adaptation setting demonstrates the effectiveness of the non-consecutive CNN model, leading to the significant performance improvement over the current state-of-the-art systems.