This paper introduces a generalised version ofimportance subsamplingfor time series reduction/aggregation inoptimisation-based power system planning models. Recent studiesindicate that reliably determining optimal electricity (investment)strategy under climate variability requires the consideration ofmultiple years of demand and weather data. However, solvingplanning models over long simulation lengths is typically com-putationally unfeasible, and established time series reductionapproaches induce significant errors. Theimportance subsamplingmethod reliably estimates long-term planning model outputs atgreatly reduced computational cost, allowing the considerationof multi-decadal samples. The key innovation is a systematicidentification and preservation of relevant extreme events inmodeling subsamples. Simulation studies on generation andtransmission expansion planning models illustrate the method’senhanced performance over established “representative days”clustering approaches. The models, data and sample code aremade available as open-source software.