BetaBetaDataset
To provide a statistical method which downscales climate models globally from coarse resolution to a higher spatial resolution.
Multivariate Adaptive Constructed Analogs (MACA) is a statistical method for downscaling Global Climate Models (GCMs) from their native coarse resolution to a higher spatial resolution that captures reflects observed patterns of daily near-surface meteorology and simulated changes in GCMs experiments.
The indicators downscaled are: 2-m maximum/minimum temperature, 2-m maximum/minimum relative humidity, 10-m zonal and meridional wind, downward shortwave radiation at the surface, 2-m specific humidity, and precipitation accumulation daily.
A multi-step procedure that uses bias correction procedures and constructed analogs approach is applied for developing the fine-scale spatial pattern using a library of observed patterns.