In this paper I present new methods for bias adjustment and statistical downscaling that are tailored to the requirements of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In comparison to their predecessors, the new methods allow for a more robust bias adjustment of extreme values, preserve trends more accurately across quantiles, and facilitate a clearer separation of bias adjustment and statistical downscaling. The new statistical downscaling method is stochastic and better at adjusting spatial variability than the old interpolation method. Improvements in bias adjustment and trend preservation are demonstrated in a cross-validation framework
VALUE is a network that developed a framework to evaluate statistical downscaling methods including ...
This is the code base used for the bias correction of climate model output data within the Inter-Sec...
Much of our knowledge about future changes in precipitation relies on global (GCM) and/or regional c...
Statistical bias correction is commonly applied within climate impact modelling to correct climate m...
International audienceA novel climate downscaling methodology that attempts to correct climate simul...
Statistical bias correction is commonly applied within climate impact modelling to correct climate m...
High-resolution, bias-corrected climate data is necessary for climate impact studies and modeling ef...
Statistical downscaling and bias correction are becoming standard tools in climate impact studies. T...
One major concern of climate modeling is the projection of future extreme events as they have the mo...
The increasing demand for high-resolution climate information has attracted growing attention to sta...
This is the code base used for bias adjustment and statistical downscaling in phase 3 of the Inter-S...
Along with the higher demand for bias-corrected data for climate impact studies, the number of avail...
In applications of climate information, coarse-resolution climate projections commonly need to be do...
VALUE is a network that developed a framework to evaluate statistical downscaling methods including ...
This is the code base used for the bias correction of climate model output data within the Inter-Sec...
Much of our knowledge about future changes in precipitation relies on global (GCM) and/or regional c...
Statistical bias correction is commonly applied within climate impact modelling to correct climate m...
International audienceA novel climate downscaling methodology that attempts to correct climate simul...
Statistical bias correction is commonly applied within climate impact modelling to correct climate m...
High-resolution, bias-corrected climate data is necessary for climate impact studies and modeling ef...
Statistical downscaling and bias correction are becoming standard tools in climate impact studies. T...
One major concern of climate modeling is the projection of future extreme events as they have the mo...
The increasing demand for high-resolution climate information has attracted growing attention to sta...
This is the code base used for bias adjustment and statistical downscaling in phase 3 of the Inter-S...
Along with the higher demand for bias-corrected data for climate impact studies, the number of avail...
In applications of climate information, coarse-resolution climate projections commonly need to be do...
VALUE is a network that developed a framework to evaluate statistical downscaling methods including ...
This is the code base used for the bias correction of climate model output data within the Inter-Sec...
Much of our knowledge about future changes in precipitation relies on global (GCM) and/or regional c...