Are seasonal soil moisture forecasts a reliable source for agricultural drought predictions in Germany?

Increasing summer drought periods in Central Europe represent a serious threat for rain-fed agriculture. Recent drought events in 2018, 2019, 2020 and 2022 cause yield loss in Germany for wheat, corn, sugar beet and grassland production. In the future, climate projections show further increase of drought periods due to climate change. Hence, the requirement of long-range drought forecasts, which could provide useful predictions for agricultural applications over several month rises. Seasonal forecasts could offer guidance for medium-term management adjustments like irrigation planning or reduced fertilizer usage in case of expected severe drought periods. Unfortunately, long range precipitation forecasts often exhibit lower prediction skill. Here we assess another relevant parameter for drought prediction, the soil moisture, for its long range predictability.  Due to the small variability and persistence of soil moisture values, it is proposed, that this storage variable is well suited for climate services like agricultural drought predicting systems on seasonal time scales. We present a coupled modelling attempt, that combines seasonal forecasts from the German Climate Forecast System (GSFS) with the soil-vegetation-atmosphere-transfer (SVAT) impact model AMBAV to simulate the soil moisture for topsoil layers on a down scaled 5x5 km grid in Germany. A quality assessment of forecast ensemble means has been done with the corresponding hindcasts for the preceding 30 years. We used the mean squared error skill score (MSESS) of monthly averages to compare soil moisture forecasts in the upper 60 cm against long term reference climatology and other parameters, like precipitation. The results of this study reveal an adequate forecast skill over lead times up to several months. This shows the potential of seasonal soil moisture forecasts for agricultural applications, like fertilization advisement and drought prediction. Overall, the impact modelling system might contribute to the adaptation of agriculture to climate change in Germany.