Precipitation bias correction: A novel method combining wavelet analysis and quantile mapping (WA-QM)

While CMIP6 has made notable improvements compared to CMIP5, there are still biases present in its simulations of different climate features due to limitations in model physics and uncertainties in input data. To address these biases, effective bias correction methods need to be employed. One commonly used method is quantile mapping, which aligns the probability density function (PDF) of climate simulations with observed data. However, this method has a limitation as it fails to maintain the temporal correspondence between model predictions and observations.

To overcome this limitation, a new approach called Wavelet Analysis-Quantile Mapping (WA-QM) has been proposed. This method involves decomposing GCM simulations into different frequency bands using discrete wavelet transformation. The scaling factors of these bands are adjusted based on their correlations with observed data. Additionally, a quantile mapping procedure is applied to modify the overall PDF of the simulations.

The WA-QM method was tested in monthly precipitation simulation data from five CMIP6 models covering the period 1951-2010 in the Pan Third Pole (PTP) region, which includes the Tibetan Plateau, Central Asia, and Southeast Asia. Results showed that WA-QM combines the advantages of wavelet analysis and quantile mapping. It effectively improves the representation of seasonal and monthly climatology varying through wavelet analysis, while also correcting mean and variance biases using quantile mapping. Consequently, the PDF closely resembles the observed data. The effectiveness of the WA-QM approach extends to correcting precipitation biases across different spatial areas and CMIP6 models.