Gaps in urban meteorological time series complicate the analysis and usage of datasets. Various gap-filling techniques exist, including the debiasing of ERA5 reanalysis data. Unfortunately, an extensive evaluation of these debiasing techniques is lacking for urban datasets. This research compares five gap-filling techniques for urban temperature time series, including three debiasing techniques that employ a learning period and time window to take into account the seasonal and diurnal ERA5 temperature bias. The evaluation, performed by filling manually constructed gaps, reveals that short gaps are best filled by linear interpolation, while longer gaps benefit from ERA5 debiasing. The bias correction is crucial for urban locations, with all debiasing techniques performing similarly. The exact length and placement of the learning period and time window have limited impact on the performance, however a symmetrical placement of the learning period with a minimum length of 10 days and a small time window provide the best outcome. Based on these results, a gap-filling algorithm is designed which efficiently fills all gaps in temperature time series by selecting the most optimal technique for each gap. The algorithm can reproduce the urban heat island effect, although a small over- or underestimation might occur.