Statistical post-processing techniques for weather, climate, and hydrological forecasts are powerful approaches to compensate for effects of errors in model structure or initial conditions, and to calibrate inaccurately dispersed ensembles. These techniques are now an integral part of many forecasting suites and are employed in many end-user applications such as wind energy production or flood warning systems. Many of these techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias correction up to very sophisticated distribution-adjusting techniques that take into account correlations among the prognostic variables.

Conversely, there is more and more interest in building probabilistic forecasts based on various sources of information at various timescales, for instance data-driven nowcasting with numerical weather prediction models. In this context, statistical techniques are also used to provide reliable forecasts.

This special issue gathers contributions on theoretical developments in statistical post-processing and blending techniques, and on evaluations of their performances in different practical applications oriented toward environmental predictions.