Systematic and random errors of MRI measurements in conjunction with the absence of ground truth data limit the assessment of accuracy and precision of 4D flow MRI image reconstruction and other downstream tasks. In this work, we propose to generate scalable synthetic CFD-enhanced aortic 4D flow MRI data, which we assemble into a dataset named RACLETTE. Our approach takes in-vivo 4D flow MRI data as input and pairs it with CFD-based “ground-truth” mean and turbulent flow fields. Specifically, high-resolution pulsatile velocity-vector and turbulent flow fields are simulated for varying degrees of aortic stenosis for a set of 139 time-resolved compliant aortic geometries. To generate realistic datasets, the synthetic flow fields are projected and embedded into the background of the in-vivo 4D flow MRI scans. Upon Fourier transform, data sampling using a given velocity encoding and undersampling scheme yields k-space data as input to deep-learning image reconstruction, segmentation and other downstream tasks. Since the synthetic 4D flow MRI data is paired with noise-free reference values including velocity, pressure, wall shear stress, the Reynolds stress tensor and pulse wave velocity, accuracy and precision of reconstruction and inference are readily available. To demonstrate the value of synthetic CFD-enhanced 4D flow MRI data, we utilize the dataset to train and apply (1) deep-learning based image reconstruction and (2) automatic vessel segmentation. It is shown that the synthetically trained deep-learning tasks generalize sufficiently and provide insights into the performance of reconstruction and processing tasks, indicating the potential value of our synthetic dataset also for further applications.