As energy systems rely more heavily on weather-dependent renewables, the choice of meteorological input data becomes increasingly consequential for long-term planning. Many energy system models simulate future systems with a single year as a proxy for “typical” future conditions. When multi-decadal weather data are available, this raises a practical question: which year should be selected so that key weather-driven variables are representative of the long-run conditions relevant for capacity expansion and investment decisions? And if multiple weather years are considered, how should they be collectively used to make investment decisions? In this paper, we show that individual weather years can differ markedly from long-term behavior in ways that matter for renewable availability (wind, solar, and hydro) and temperature-driven demand.

Using multi-decadal pan-European weather data, we translate meteorological variables into energy system relevant variables like wind, solar and hydro generation and heating demand profiles through a consistent weather-to-energy modelling framework. We quantify interannual variability across these weather-dependent components, focusing on deviations from long-term averages and distributions. We then compare outcomes derived from a single weather year with outcomes obtained from 40 years of weather data, considering both the weather driven supply and demand data and results from a pan-European capacity expansion model towards 2050. We find that some years exhibit continent-wide anomalies and systematically low or high renewable availability across large areas, biasing aggregate annual indicators. Other years appear near-average at the system level because regional anomalies cancel out, while masking substantial regional over- and under-representation of resources and demand.

Overall, system-level annual indicators alone are insufficient to assess whether a weather year is representative of typical long-run conditions. A single realization can appear near average in aggregate while misrepresenting the distribution and spatial allocation of renewable availability and temperature-driven demand. Used in isolation, such anomalies can affect technology choices, regional capacity allocation, and infrastructure needs. Multiple weather years can be applied in capacity expansion modelling; however, it is not straightforward to derive overall investment results as each weather year will drive unique optimal investments. We discuss adjusting single-year inputs based on multi-decade averages and distributions of weather-driven energy variables as an option for using long-term weather information in capacity expansion modelling while keeping computational burden manageable.