Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments.Energies 2020, 13, 1786 2 of 17 for individual power plants. There are several explanations for this approach: (i) modelling tends to be thought of as a bottom-up approach, therefore starting from individual power plants (to e.g., assist with their operations) rather than at the entire country or pan-European level; (ii) the impacts of climate variables on hydro power are varied and complex, and many factors contribute to these impacts such as watershed characteristics, the river flow rate, the evaporation process, and human factors, and hence, using only climate data is not sufficient to predict hydro power generation at the desired accuracy for plant-level operational purposes; (iii) hydro power generation at the European level has only recently started to become publicly available (as further discussed below); (iv) the quality of climate data as provided by reanalysis (i.e., climate reconstructions), and particularly precipitation, has been improving significantly only in recent years.Wind and solar power play an increasingly important role in a low-carbon energy plan. However, their weather-dependent nature requires a more stable energy source, together with storage solutions, to cope with climate variability. Hydro power is a good candidate with its ability to dispatch or store electricity according to demand. For example, [4] sho...