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Statistical analyses and descriptive characterizations are sometimes assumedto be offering information on time series forecastability. Despite thescientific interest suggested by such assumptions, the relationships betweendescriptive time series features (e.g., temporal dependence, entropy,seasonality, trend and linearity features) and actual time seriesforecastability (quantified by issuing and assessing forecasts for the past)are scarcely studied and quantified in the literature. In this work, we aim tofill in this gap by investigating such relationships, and the way that they canbe 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 descriptivefeatures and nine seasonal time series forecasting methods (i.e., one simple,five exponential smoothing, two state space and one automated autoregressivefractionally integrated moving average methods). We apply this framework tothree global datasets originating from the larger Global Historical ClimatologyNetwork (GHCN) and Global Streamflow Indices and Metadata (GSIM) archives. Asthese datasets comprise over 13 000 monthly temperature, precipitation andriver flow time series from several continents and hydroclimatic regimes, theyallow us to provide trustable characterizations and interpretations of 12-monthahead hydroclimatic forecastability at the global scale...

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale