Accurate, localised rainfall information is essential for applications such as 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. Bias correction of daily rainfall is particularly challenging due to its complex characteristics. 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 directly into existing bias correction methods through state-dependent rain day thresholds and rainfall adjustments, aimed at improving the temporal structure of rainfall. 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 outperformed LOCI and QM by improving the representation of rainfall persistence, onset, and wet and dry spell characteristics, while maintaining improvements in rain day frequency and overall rainfall statistics. These results demonstrate that the proposed methods could be beneficial for applications such as crop simulation, hydrological modelling and other applications which rely on accurate representation of rainfall sequencing.

翻译：准确的局地降雨信息对于农业规划、气候风险评估和水资源管理等应用至关重要。网格化气候产品能提供大面积的降雨信息,但在局地尺度上可能缺乏所需的精度,通常需要在用于局地影响研究之前进行偏差校正。由于日降雨量的复杂特征,其偏差校正尤为具有挑战性。局地强度缩放法(LOCI)和分位数映射法(QM)是两种广泛使用的偏差校正方法,它们可同时调整降雨频率和强度,但未考虑日降雨的时间结构。这可能导致干湿期的表征出现偏差。本研究提出将两态一阶马尔可夫链直接整合到现有偏差校正方法中,通过状态依赖的雨日阈值和降雨量调整,旨在改善降雨的时间结构。该框架的两种实现方法被提出:马尔可夫链局地强度缩放法(MC LOCI)和马尔可夫链分位数映射法(MC QM)。所提方法被应用于AgERA5再分析数据及津巴布韦五个站点的降雨数据。结果表明,马尔可夫链方法在保持雨日频率和整体降雨统计量改善的同时,通过改进降雨持续性、起始期以及干湿期特征的表征,优于LOCI和QM方法。这些结果证明,所提方法可有助于依赖降雨序列准确表征的应用,如作物模拟、水文建模及其他相关领域。