Evaluating seasonal forecasts in Austria using agrometeorological indicators

方法

降尺度温度与降水预报，验证六个作物无关农业气象指标的预报技巧。

发现

温度降尺度有效、降水提升有限；有效积温表现最佳，热浪日误报率高。

机会点

可研究极端天气指标的区域校准与季节预报嵌入农业决策支持系统。

Abstract Farmers rely on weather information to guide crop management; however, outcomes often remain uncertain until harvest. This pilot study evaluated downscaled medium- to seasonal-range forecasts for Austria (2018–2022), focusing on air temperature, precipitation, and six crop-independent agrometeorological indicators (stress and suitability metrics). Spatial downscaling enhanced temperature forecast performance, whereas gains for precipitation were limited. Agrometeorological indicators generally showed higher forecast accuracy at shorter lead times, although the magnitude of this effect varied among indicators. Warm springs in 2018–2019 led to temperature underestimation and reduced springtime accuracy, while Frost Stress Days were affected by a systematic cold bias during the study period. Indicators based on moderate temperature thresholds showed more robust performance than extreme-based ones. Sum of Effective Growing Temperature performed best, particularly for June-start forecasts, whereas Heat Wave Days showed limited operational suitability because of high false-alarm rates. Regional differences in forecast errors were significant for several indicators, with their magnitude and direction varying across climate regions. Site-level MAE was also significantly associated with altitude, mean temperature, and precipitation for several indicator-forecast combinations. For potential water balance, forecasts underestimated available water, consistent with combined temperature and precipitation biases. To enhance agricultural applicability, future studies should improve the accuracy of extreme-weather forecasts, calibrate models regionally, and integrate seasonal forecasts into user-friendly decision support systems. Given the short verification period (2018–2022), the results provide preliminary evidence of forecasting skill under the climatic conditions represented in the study period rather than robust long-term predictability estimates. Preprint available at https://zenodo.org/records/18890040 .