Weather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding the weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products that mitigate financial losses for farmers, but suffer from considerable basis risk. In this study, an artificial neural network is set up and calibrated to a rich set of farm-level yield data in Germany covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, serves as a benchmark. The empirical application reveals that the gain in forecasting precision by using machine learning techniques compared with traditional estimation approaches is substantial and that the use of regionalized models and disaggregated high-resolution weather data improve the performance of artificial neural networks.