When CWS accuracy is high on the training data, the NER model trained with such data puts more weight on word segmentation features rather than character features.
during testing, the performance of CWS drops, resulting in more word segmentation errors, with a high chance to propagate to NER errors; even worse, a lot of these CWS errors are around NERs since a lot of NERs are OOVs and thus are challenging to segment correctly.