Traditionally, ECM fits are used to extract parameters based on a priori knowledge about features of the EIS spectrum of an investigted system, yet this method has certain drawbacks. In particular, finding the correct number and type of elements, such that each element has a physical meaning or is justified as an individual component by the data, is non-trivial. Furthermore, a numerical ECM fit of even an ideal model may converge in a local minimum that makes little physical sense. Fit quality alone is a poor indicator for the scientific utility of the fit. Choosing good initial values and constraints for the fitted parameters is required to obtain physically meaningful results. This may be possible by noticing that different ECM elements depict features at different frequencies in the DRT. A DRT can thus be a useful preparation step for ECM fitting. For one, no prior assumption regarding the number and type of circuit elements is necessary. On the contrary, a DRT provides information regarding the number of elements that can be separated purely based on the data, which can be especially helpful to define the number and values of ZARC elements in a model. Additionally, the DRT technique can also give information regarding the noise bandwidth and a soft boundary of its parameters. For diffusion processes and the parametrization of Warburg elements, DRT with a Debye kernel may not be most suitable , but an empirical fit is possible. Therefore the statistics of the residuals does not get degraded by the presence of processes giving rise to Warburg behavior .