Enhanced Quantile Delta Mapping for Bias Correction in Climate Modeling

Music City Center 

Global climate models (GCMs) are essential tools for simulating the complex interactions among atmospheric, terrestrial, and oceanic processes, yet they frequently exhibit systematic biases, especially in precipitation estimates. Often, GCMs overestimate rainfall frequency and fail to capture extreme events accurately. Although Quantile Delta Mapping (QDM) is widely used for bias correction, its reliance on empirical distributions can lead to instability at high quantiles due to limited data. To overcome these challenges, we propose an enhanced QDM approach that integrates spatial basis functions to adjust data from multiple observational sites simultaneously while incorporating a generalized Pareto distribution to model extreme precipitation tails. By borrowing information across locations, our method reduces high-quantile instability and improves bias correction. We demonstrate the effectiveness of our approach using daily precipitation projections from ECMWF Reanalysis v5 (ERA5) over Taiwan during the summer.

extreme precipitation

generalized Pareto distribution 

Section on Statistics and the Environment