Contrail cirrus clouds persisting in ice-supersaturated air cause a substantial fraction of aviation's climate impact. One proposed method for the mitigation of this impact involves modifying flight paths to avoid particular regions of the atmosphere that are conducive to the formation of persistent contrails. Ascertaining which flight formed each observed contrail can be used to assess and improve contrail forecast models, as well as study the effectiveness of performing contrail avoidance. The problem of contrail-to-flight attribution is complicated by several factors, such as the time required for a contrail to become visible in satellite imagery, high air traffic densities and errors in wind data. Recent work has introduced automated algorithms for solving the attribution problem, but lack an evaluation against ground-truth data. In this work, we present a method for producing synthetic contrail observations with predetermined contrail-to-flight attributions which can be used to evaluate – or "benchmark" – and improve such attribution algorithms. The resulting performance metrics can be used to understand the implications of using this observational data in downstream tasks such as forecast model evaluation and analysis of contrail avoidance trials. We also introduce a novel, highly-scalable, contrail-to-flight attribution algorithm that leverages the characteristic compounding of error induced by simulating contrail advection using numerical weather models. The benchmark shows an improvement of about 30 % in precision versus previous contrail-to-flight attribution algorithms, without compromising recall.