Farmers rely on weather information to guide crop management, yet outcomes often remain uncertain until harvest. This study evaluates downscaled medium to seasonal-range forecasts for Austria from 2018 to 2022, focusing on air temperature, precipitation and six crop-independent agrometeorological stress and suitability indicators. Spatial downscaling markedly improves temperature forecasts, while improvements for precipitation remain limited. For the agrometeorological indicators, forecast accuracy generally increases as lead time shortens, reflecting higher reliability closer to the target season. Unusually warm springs in 2018 and 2019 led to underestimation of temperature and lower springtime accuracy. Indicators based on moderate temperature thresholds outperform those tied to extremes; notably, the Sum of Effective Growing Temperature achieves its strongest performance for June‑start forecasts, having already performed consistently well for April‑start forecasts. Regional differences are evident: winter indicators perform better in lowlands with fewer extreme cold events, whereas summer indicators are most accurate in Alpine regions where high-temperature extremes are less frequent. For the potential water balance, forecasts systematically underestimated available water, most notably in the Pannonian and Illyrian regions, driven by a warm-summer bias and a likely dry bias. Short lead times can inform in-season decisions, but usefulness declines at longer lead times when high precision is required. To enhance practical value for agriculture, future efforts should improve the accuracy of extreme-weather forecasts, calibrate models to regional conditions and integrate forecasts into user-friendly decision support systems co-developed with farmers and advisers.