  Statistical analyses and descriptive characterizations are sometimes assumed
to be offering information on time series forecastability. Despite the
scientific interest suggested by such assumptions, the relationships between
descriptive time series features (e.g., temporal dependence, entropy,
seasonality, trend and linearity features) and actual time series
forecastability (quantified by issuing and assessing forecasts for the past)
are scarcely studied and quantified in the literature. In this work, we aim to
fill in this gap by investigating such relationships, and the way that they can
be exploited for understanding hydroclimatic forecastability and its patterns.
To this end, we follow a systematic framework bringing together a variety of --
mostly new for hydrology -- concepts and methods, including 57 descriptive
features and nine seasonal time series forecasting methods (i.e., one simple,
five exponential smoothing, two state space and one automated autoregressive
fractionally integrated moving average methods). We apply this framework to
three global datasets originating from the larger Global Historical Climatology
Network (GHCN) and Global Streamflow Indices and Metadata (GSIM) archives. As
these datasets comprise over 13 000 monthly temperature, precipitation and
river flow time series from several continents and hydroclimatic regimes, they
allow us to provide trustable characterizations and interpretations of 12-month
ahead hydroclimatic forecastability at the global scale...
