Advancements and challenges in bias correction quantile mapping for climate projections: a comprehensive review

Bias correction quantile mapping (BCQM) has become a pivotal tool in climate science, particularly for refining the outputs of Global Climate Models (GCMs) and Regional Climate Models (RCMs) at local scales. While GCM outputs are invaluable for understanding climate change, their coarse resolution introduces uncertainties requiring downscaling techniques like BCQM. This review paper explores the advancements, practical applications, and limitations of BCQM methods, emphasizing their critical role in improving climate projections. BCQM operates by mapping observed data distributions to model outputs, thereby correcting biases and enhancing model accuracy. Recent developments have led to significant improvements, such as the successful application of multivariate BCQM in capturing complex climate interactions and hybrid empirical BCQM techniques that improve performance in extreme climate conditions. These methods have been effectively implemented in diverse regions, leading to more accurate temperature and precipitation projections, which support critical sectors like agriculture, water resource management, and disaster preparedness. Furthermore, BCQM has been instrumental in refining seasonal forecasts and long-term climate projections, providing valuable insights for policymakers and stakeholders. Despite these advancements, BCQM still faces challenges, such as the inability to correct for inherited GCM errors, poor representation of wet/dry spell occurrences, and limitations in extreme event correction. The review highlights the need for further research to address these challenges, particularly in the context of extreme climate events and non-stationarity biases. The paper calls for more robust BCQM methods that can handle the increasing complexity and volume of climate data, offering reliable projections for future climate scenarios. By refining BCQM techniques and incorporating additional climate factors, researchers can improve the accuracy and dependability of climate projections, ultimately aiding in better decision-making and risk assessment in the face of climate change.

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