Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

On the need for bias correction in regional climate scenarios

Introduction Conclusions References

A novel bias correction methodology for climate impact simulations

Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies ...

A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM

Precipitation bias correction of very high resolution regional climate models

Regional climate models are prone to biases in precipitation that are problematic for use in impact models such as hydrology models. A large number of methods have already been proposed aimed at correcting various moments of the rainfall distribution. They all require that the model produce the same or a higher number of rain days than the observational data sets, which are usually gridded data...

Addressing the mischaracterization of extreme rainfall in regional climate model simulations – A synoptic pattern based bias correction approach

Addressing systematic biases in regional climate model simulations of extreme rainfall is a necessary first step before assessing changes in future rainfall extremes. Commonly used bias correction methods are designed to match statistics of the overall simulated rainfall with observations. This assumes that change in the mix of different types of extreme rainfall events (i.e. convective and non...