  Computer-generated forecasts divide the earth's surface into gridboxes, each
now ~25% of the size of London, and predict one value per gridbox. If weather
varies markedly within a gridbox forecasts for specific sites inevitably fail.
A completely new statistical post-processing method, using ensemble forecasts
as input, anticipates two gridbox-weather-dependant factors: degree of
variation in each gridbox, and bias on the gridbox scale. Globally, skill
improves substantially; for extreme rainfall, for example, useful forecasts
extend 5 days ahead. Without post-processing this limit is < 1 day. Relative to
historical forecasting advances this constitutes ground-breaking progress. The
key drivers, incorporated during calibration, are meteorological understanding
and abandoning classical notions that only local data be used. Instead we
simply recognise that "showers are showers, wherever they occur worldwide"
which delivers a huge increase in calibration dataset size. Numerous
multi-faceted applications include improved flash flood warnings,
physics-related insights into model weaknesses and global pointwise
re-analyses.
