Analog methods (AMs) have long been used for precipitation prediction and climate studies. However, they rely on manual selections of parameters, such as the predictor variables and analogy criterion. Previous work showed the potential of genetic algorithms (GAs) to optimize most parameters of AMs. This research goes one step further and investigates the potential of GAs for automating the selection of the input variables and the analogy criteria (distance metric between two data fields) in AMs. Our study focuses on daily precipitation prediction in central Europe, specifically Switzerland, as a representative case. Comparative analysis against established reference methods demonstrates the superiority of the GA-optimized AM in terms of predictive accuracy. The selected input variables exhibit strong associations with key meteorological processes that influence precipitation generation. Further, we identify a new analogy criterion inspired by the Teweles-Wobus criterion, but applied directly to grid values, which consistently performs better than other Euclidean distances. It shows potential for further exploration regarding its unique characteristics. In contrast to conventional stepwise selection approaches, the GA-optimized AM displays a preference for a flatter structure, characterized by a single level of analogy and an increased number of variables. Although the GA optimization process is computationally intensive, we highlight the use of GPU-based computations to significantly reduce computation time. Overall, our study demonstrates the successful application of GAs in automating input variable selection for AMs, with potential implications for application in diverse locations and data exploration for predicting alternative predictands.