Abstract Installation of offshore wind farms (OWFs) is becoming increasingly important to ensure a reduction in greenhouse gas emissions; however, OWFs also pose a threat to migrating birds and other wildlife. Informed marine spatial planning is therefore crucial, but individual‐based high‐resolution data on bird migration across the sea are currently lacking. We equipped 51 individuals of the near threatened Eurasian curlew Numenius arquata with GPS tags (118 flight tracks) across multiple years and countries to assess their four‐dimensional migration routes across the Baltic Sea (i.e. flight tracks, altitudes, phenology and diurnal patterns), to inform collision‐risk models and assess potential conflicts with current and future OWFs. Despite a broad‐front migration, we identified core migration areas in the south‐western Baltic Sea (and adjacent mainland), largely overlapping with already operating OWFs. Generalized linear models based on a resampling procedure to overcome autocorrelation of tracking data showed that flight altitudes across the sea and during autumn (median: 60 m) were significantly lower than those across land (median: 335 m) and during spring (median across sea: 150; median across land: 576 m). Across the sea, curlews spent 74.8% and 62.2% of their migration times below 300 m during autumn and spring, respectively, indicating a potentially high collision risk with OWFs. The mean flight speed was 56.3 km/h (±20.3 km/h). Migration intensity was highest at night over a 10‐day period during April, suggesting that restricted turbine operation for several days might be a possible management measure. Our study showed that, even for broad‐front migrants, it is possible to identify particularly sensitive sea areas deserving special protection enabling a sound marine spatial planning. This is a crucial finding also for various other shorebirds on the East Atlantic Flyway. Further studies are needed to assess the behavioural reactions of migrating birds with respect to OWFs using high‐resolution tracking data.