Predictive coding has been proposed as a unifying theory of brain function. However, few studies have examined this theory during complex cognitive processing across multiple time-scales and levels of abstraction. We used MEG, EEG and fMRI to ask whether dynamic, hierarchical predictive coding can account for the timecourse of evoked activity at multiple cortical levels during language comprehension. Unexpected words produced increased activity in left temporal cortex (lower-level prediction error). Critically, violations of high-precision event predictions produced additional activity within left inferior frontal cortex (higher-level prediction error). Furthermore, the successful resolution of higher-level prediction error led to later feedback to temporal cortex (top-down sharpening), while a failure to resolve these errors led to sustained activity at still lower levels (reanalysis). These findings suggest that fundamental principles of dynamic hierarchical predictive coding –– suppression of prediction error, precision-weighting, delayed top-down sharpening –– can explain the dynamics of neural activity during human language comprehension.