With the introduction in Europe of drive cycles such as RDE and WLTC, transient emissions prediction is more challenging than for the NEDC. Transient predictions are used in the calibration optimisation process to determine the cumulative cycle emissions for the purpose of meeting objectives and constraints. Predicting emissions such as soot accurately is the most difficult area, because soot emissions rise very steeply during certain transients. Besides model accuracy, prediction time also is also important when applying a dynamic model because the optimisation process can take a significant amount of time to converge to a solution that satisfies all constraints. The method proposed in this paper is an evolution of prediction using a steady state global model. A dynamic model can provide the instantaneous prediction of boost and EGR that a static model cannot. Meanwhile, a static model is more accurate for steady state engine emissions. Combining these two model types allows more accurate prediction of emissions against time. A global dynamic model combines a dynamic model of the engine air path with a static DOE (Design of Experiment) emission model. The dynamic model is constructed using a Volterra series model for the EGR response and a Stochastic Process Model (SPM) model for boost pressure. Both models are trained using data collected from APRBS (Amplitude Modulated Pseudo Random Binary Sequences) tests. The static model is an SPM model trained using data collected in a steady state DOE test. The output of the global dynamic model is an accurate prediction of engine emissions with a drive cycle as model input. The global dynamic model is called every iteration during the calibration optimisation process and the cycle cumulative results are used to control the constraints and optimise the objectives. This produces better final calibration maps ready for immediate vehicle tests without test bed validation, thereby improving the efficiency of the calibration process.