This research assesses the efficacy of thirteen bias correction methods, including traditional and machine learning-based approaches, in downscaling four chosen GCMs of Coupled Model Intercomparison Project 6 (CMIP6) in Nigeria. The 0.5° resolution gridded rainfall, maximum temperature (Tmx), and minimum temperature (Tmn) of the Climate Research Unit (CRU) for the period 1975 − 2014 was used as the reference. The Compromise Programming Index (CPI) was used to assess the performance of bias correction methods based on three statistical metrics. The optimal bias-correction technique was employed to rectify bias to project the spatiotemporal variations in rainfall, Tmx, and Tmn over Nigeria for two distinct future timeframes: the near future (2021–2059) and the distant future (2060–2099). The study's findings indicate that the Random Forest (RF) machine learning technique better corrects the bias of all three climate variables for the chosen GCMs. The CPI of RF for rainfall, Tmx, and Tmn were 0.62, 0.0, and 0.0, followed by the Power Transformation approach with CPI of 0.74, 0.36, and 0.29, respectively. The geographic distribution of rainfall and temperatures significantly improved compared to the original GCMs using RF. The mean bias-corrected projections from the multimodel ensemble of the GCMs indicated a rainfall increase in the near future, particularly in the north by 2.7–12.7%, while a reduction in the south in the far future by -3.3% to -10% for different SSPs. The temperature projections indicated a rise in the Tmx and Tm from 0.71 °C and 0.63 °C for SSP126 to 2.71 °C and 3.13 °C for SSP585. This work highlights the significance of comparing bias correction approaches to determine the most suitable approach for adjusting biases in GCM estimations for climate change research.

本研究评估了 13 种偏差校正方法（包括传统方法和基于机器学习的方法）在缩小尼日利亚耦合模型比对项目 6 (CMIP6) 的四个选定 GCM 方面的有效性。以气候研究中心（CRU）1975~2014年期间0.5°分辨率网格降雨量、最高气温（Tmx）和最低气温（Tmn）为参考。妥协编程指数（CPI）用于评估基于三个统计指标的偏差校正方法的性能。采用最佳偏差校正技术来纠正偏差，以预测尼日利亚在两个不同的未来时间范围内降雨量、Tmx 和 Tmn 的时空变化：近期（2021-2059 年）和遥远的未来（2060-2099 年）。研究结果表明，随机森林 (RF) 机器学习技术可以更好地纠正所选 GCM 的所有三个气候变量的偏差。RF 的降雨量、Tmx 和 Tmn 的 CPI 分别为 0.62、0.0 和 0.0，其次是功率变换方法，CPI 分别为 0.74、0.36 和 0.29。与使用 RF 的原始 GCM 相比，降雨量和温度的地理分布显着改善。GCM 多模式系综的平均偏差校正预测表明，在不久的将来，降雨量将增加，特别是在北部，增加 2.7-12.7%，而在不久的将来，南部的降雨量将减少 -3.3% 到 -10%对于不同的 SSP。温度预测表明，SSP126 的 Tmx 和 Tm 从 0.71 °C 和 0.63 °C 上升至 SSP585 的 2.71 °C 和 3.13 °C。这项工作强调了比较偏差校正方法以确定调整气候变化研究 GCM 估计偏差的最合适方法的重要性。