“…The quality of seasonal streamflow forecasts relies on a forecasting chain that includes at least seasonal meteorological forcing, initialization of hydrological model states and a hydrologic model setup (Mazrooei et al, 2015; Pechlivanidis et al, 2014). To improve the forecast quality and further the decision‐making, this chain can be advanced by introducing additional components that allow assimilation of data to set the initial model states (e.g., in situ/Earth observations of soil moisture and snow water equivalent; Draper & Reichle, 2015; Griessinger et al, 2016; Liu et al, 2012; Musuuza et al, 2020), postprocessing of seasonal meteorological forecasts (e.g., bias adjustment and model output statistics; Dobrynin et al, 2018; Manzanas et al, 2019; Zhao et al, 2017), and postprocessing of hydrologic forecasts (e.g., conditioning to local data; Lucatero et al, 2018; Madadgar et al, 2014; Wood & Schaake, 2008). Currently, forecast service development is ad hoc with improvements made to single parts of the forecasting chain when and where available, and with only very limited guidance on the relative importance of each component to the forecasting chain performance (Arheimer et al, 2011; Sinha et al, 2014; Thiboult et al, 2016).…”