Improving Bias Correction Methods for Daily Rainfall Using a Markov Chain Approach

Accurate, localised rainfall information is essential for agricultural planning, climate risk assessment, and water resources management.

Gridded climate products provide rainfall information over large areas but can lack the accuracy needed at local scales, often requiring bias correction before use in local impact studies.

Local intensity scaling (LOCI) and quantile mapping (QM) are two widely used bias correction methods which adjust both rainfall frequency and intensity, but do not account for the temporal structure of daily rainfall.

This can lead to biases in the representation of wet and dry spells.

This study proposes integrating a two-state first-order Markov chain into existing bias correction methods through state-dependent rain day thresholds and rainfall adjustments, aimed at improving temporal structure.

Two implementations of this framework are presented: Markov chain local intensity scaling (MC LOCI) and Markov chain quantile mapping (MC QM).

The proposed methods were applied to AgERA5 reanalysis data with rainfall data from five stations in Zimbabwe.

Results showed that the Markov chain methods improved the representation of rainfall persistence, onset, and wet and dry spell characteristics compared to LOCI and QM, while maintaining improvements in rain day frequency, mean and total rainfall.

Improvements in event timing and daily rainfall amounts were limited.

Results from five locations in Zimbabwe demonstrate that the proposed methods could be beneficial for crop simulation, hydrological modelling and other applications requiring accurate rainfall sequencing.

Evaluation across additional regions and gridded products would establish the broader applicability of the proposed methods under a range of conditions.