Satellite and reanalysis rainfall products (SREs) can serve as valuable complements or alternatives in data-sparse regions, but their significant biases necessitate correction. This study rigorously evaluates a suite of bias correction (BC) methods, including statistical approaches (LOCI, QM), machine learning (SVR, GPR), and hybrid techniques (LOCI-GPR, QM-GPR), applied to seven SREs across 38 stations in Ghana and Zambia, aimed at assessing their performance in rainfall detection and intensity estimation. Results indicate that the ENACTS product, which uniquely integrates a large number of station records, was the most corrigible SRE; in Zambia, nearly all BC methods successfully reduced the mean error on daily rainfall amounts at over 70% of stations. However, this performance requires further validation at independent stations not incorporated into the ENACTS product. Overall, the statistical methods (QM and LOCI) generally outperformed other techniques, although QM exhibited a tendency to inflate rainfall values. All corrected SREs demonstrated a high capability for detecting dry days (POD $\ge$ 0.80), suggesting their potential utility for drought applications. A critical limitation persisted, however, as most SREs and BC methods consistently failed to improve the detection of heavy and violent rainfall events (POD $\leq$ 0.2), highlighting a crucial area for future research.

翻译：卫星与再分析降雨产品（SREs）在数据稀疏区域可作为有价值的补充或替代数据源，但其显著的偏差需要进行校正。本研究系统评估了一系列偏差校正（BC）方法，包括统计方法（LOCI、QM）、机器学习（SVR、GPR）及混合技术（LOCI-GPR、QM-GPR），将其应用于加纳和赞比亚38个站点的七种SREs，旨在评估这些方法在降雨检测与强度估计方面的性能。结果表明，ENACTS产品因独特整合了大量站点记录，成为可校正性最佳的SRE；在赞比亚，近90%的BC方法成功在超过70%的站点降低了日降雨量的平均误差。然而，该性能需在未纳入ENACTS产品的独立站点进一步验证。总体而言，统计方法（QM与LOCI）普遍优于其他技术，尽管QM显示出高估降雨值的趋势。所有校正后的SREs均表现出较高的干日检测能力（POD ≥ 0.80），表明其具备应用于干旱分析的潜力。但一个关键局限依然存在：大多数SREs与BC方法始终未能改进对强降雨和极端降雨事件的检测能力（POD ≤ 0.2），这凸显了未来研究的重要方向。