Wind observations collected at citizen weather stations (CWSs) could be an invaluable resource in climate and meteorology studies, yet these observations are underutilised because scientists do not have confidence in their quality. These wind speed observations have systematic biases, likely caused by improper instrumentation and station sitings. Such systematic biases introduce spatial inconsistencies that prevent comparison of these stations spatially and limit the possible usage of the data. In this paper, we address these issues by improving and developing new methods for identifying suspect observations and adjusting systematic biases. Our complete quality control and bias adjustment procedure consists of four steps: (a) performing within-station quality control tests to check the plausible range and the temporal consistency of observations, (b) adjusting the systematic bias using empirical quantile mapping, (c) implementing between-station quality control to compare observations from neighbouring stations to identify spatially inconsistent observations, and (d) providing estimates of the true wind when CWSs falsely report zero wind speeds, as a complement to the bias adjustment. We apply these methods to CWSs from the Weather Observation Website (WOW) in the Netherlands, comparing the crowdsourced data with official data, and statistically assessing the improvements in data quality after each step. The results demonstrate that the crowdsourced wind speed data are more comparable with official data after quality control checks and bias adjustment steps. Our quality assessment methods therefore give confidence in CWSs, converting their observations into a usable data product and an invaluable resource for applications in need of additional wind observations.