This study presents a long-term analysis of European photovoltaic (PV) power output using 30 years of validated hourly reanalysis and satellite data. The research uses the MERRA and MERRA-2 global meteorological reanalyses, along with the CM-SAF SARAH satellite dataset, to simulate hourly PV output across Europe. These simulations are validated against metered time series from over 1000 PV systems and national aggregate output data from transmission network operators. The results show that satellite data provides slightly better accuracy, while reanalysis data offers greater stability. The simulations are corrected for systematic bias by matching them to the mean bias in modeling individual sites, and then used to examine long-term patterns, variability, and correlations with power demand across Europe. The study finds that the increasing deployment of PV significantly changes net power demand and affects system adequacy and ramping requirements, with varying impacts across European countries. The simulation code and hourly simulations for all European countries are available via an interactive web platform, www.renewables.ninja. The study highlights the importance of high-quality renewable energy simulation data for understanding the impacts of increasing PV deployment on grid integration. It also notes that while SARAH provides higher accuracy at individual sites, it contains similar average bias as MERRA and offers similar performance when aggregated to country-level. MERRA is more consistent on a long-term seasonal basis and requires less effort to clean missing or erroneous observations. Both datasets require long-run average spatial calibration for accurate results. The study concludes that none of the data sources investigated are ideal, and that the availability of long-term simulations with higher confidence from validation is fundamental to understanding the effects of increasing PV deployment and developing strategies to address them. The study also emphasizes the need for further research to better determine the temporal and country-specific biases in PV output data.This study presents a long-term analysis of European photovoltaic (PV) power output using 30 years of validated hourly reanalysis and satellite data. The research uses the MERRA and MERRA-2 global meteorological reanalyses, along with the CM-SAF SARAH satellite dataset, to simulate hourly PV output across Europe. These simulations are validated against metered time series from over 1000 PV systems and national aggregate output data from transmission network operators. The results show that satellite data provides slightly better accuracy, while reanalysis data offers greater stability. The simulations are corrected for systematic bias by matching them to the mean bias in modeling individual sites, and then used to examine long-term patterns, variability, and correlations with power demand across Europe. The study finds that the increasing deployment of PV significantly changes net power demand and affects system adequacy and ramping requirements, with varying impacts across European countries. The simulation code and hourly simulations for all European countries are available via an interactive web platform, www.renewables.ninja. The study highlights the importance of high-quality renewable energy simulation data for understanding the impacts of increasing PV deployment on grid integration. It also notes that while SARAH provides higher accuracy at individual sites, it contains similar average bias as MERRA and offers similar performance when aggregated to country-level. MERRA is more consistent on a long-term seasonal basis and requires less effort to clean missing or erroneous observations. Both datasets require long-run average spatial calibration for accurate results. The study concludes that none of the data sources investigated are ideal, and that the availability of long-term simulations with higher confidence from validation is fundamental to understanding the effects of increasing PV deployment and developing strategies to address them. The study also emphasizes the need for further research to better determine the temporal and country-specific biases in PV output data.