<p>We used steady states obtained by a spin-up for a global marine ecosystem model as training data to build a mapping from the small number of biogeochemical model parameters onto the three-dimensional converged steady annual cycle. The mapping was performed by a conditional variational autoencoder (CVAE) with mass correction. Applied to test data, we show that the prediction obtained by the CVAE already gives a reasonably good approximation of the steady states obtained by a regular spin-up. However, the predictions do not reach the same level of annual periodicity as those obtained in the original spin-up data. Thus, we took the predictions as initial values for a spin-up. We show that the number of necessary iterations, corresponding to model years, to reach a prescribed stopping criterion in the spin-up can be significantly reduced compared to the use of the originally uniform, constant initial value. The amount of reduction depends on the applied stopping criterion, which measures the periodicity of the solution. The savings in needed model years and, thus, computing time for the spin-up range from 50 to 95%, depending on the stopping criterion for the spin-up. We compared these results with the use of the mean of the training data as an initial value. We found that this also accelerates the spin-up, but only by a much lower factor. The aim of this paper is to provide a proof of concept for two reasons. The first one is that we used a very simple ecosystem model only. The second one is that all data were already available. Thus, the cost of generating appropriate training data – which plays a significant role – was not taken into account.</p>