Most existing process compliance monitoring approaches detect compliance violations in an ex post manner. While predicate prediction methods address this by forecasting compliance violations, they offer only binary outcomes without quantifying how significantly an ongoing process instance deviates from the desired state. Measuring the magnitude of violations would provide organizations with deeper insights into operational performance, supporting informed decision-making to mitigate the risk of non-compliance. Thus, we propose two predictive compliance monitoring methods: the first transforms the binary classification into a hybrid classification-regression task, and the second leverages multi-task learning to simultaneously predict compliance status and quantify the magnitude of violation. We focus on temporal constraints as they are prevalent across domains such as healthcare. Evaluations on synthetic and real-world event logs demonstrate that our approaches enable violation quantification while maintaining comparable compliance prediction performance achieved by state-of-the-art approaches.