Monitoring machine learning systems and efficiently recovering their reliability after performance degradation are two of the most critical issues in real-world applications. However, current monitoring strategies lack the capability to provide actionable insights answering the question of why the performance of a particular model really degraded. To address this, we propose Explanatory Performance Estimation (XPE) as a novel method that facilitates more informed model monitoring and maintenance by attributing an estimated performance change to interpretable input features. We demonstrate the superiority of our approach compared to natural baselines on different data sets. We also discuss how the generated results lead to valuable insights that can reveal potential root causes for model deterioration and guide toward actionable countermeasures.