An ensemble probability forecast of rainfall > 10 mm

Regional (12km) and Local (2.2km) Projections

Using the regional and local projections

The regional projections cover Europe and are driven from the Hadley Centre climate model subset of the global projections, resulting in 12 simulations at 12km. The local projections, which cover the UK only, are in turn driven by the regional projections. Both datasets can support a wide range of impacts studies and provide data with full spatial and temporal coherence and a wide range of variables and time resolutions.  These are important for applications requiring assessment of multiple drivers of changing hazards, especially at higher spatial resolution.

You should use the regional projections if you’re interested in:

  • Applications where local scales are essential, as the regional projections better represent local effects due to land elevation, coastlines and surface characteristics, as well as providing improved resolution of dynamical features such as mesoscale circulations and frontal systems.
  • Improved simulation of extremes with higher temporal variability (e.g. daily, subdaily for UKCP Local (2.2km)).
  • Extremes that explore future outcomes outside of the 90th percentile of the probabilistic projections (e.g. changes in long-term average precipitation showing large increases in winter in some western coastal regions).
  • Analysing climate at multiple geographical locations at the same time, i.e. requiring coherence in space (and across variables).
  • Using daily data and being able to calculate a larger set of metrics than that available in the probabilistic projections.
  • Analysing drivers and impacts of year-to-year variability.  The regional and local projections support investigation of resulting high-impact events.

Further information

Further supporting information can be found in Guidance and Science Reports and include:

The full dataset is available from the UKCP18 User Interface and CEDA Data Catalogue (note that this requires familiarity with handling large datasets).