Statistical downscaling (SD) and bias adjustment (BA) methods are routinely used to produce regional to local climate change projections from coarse global model outputs. The suitability of these techniques depends on the particular application of interest and, especially, on the required spatial resolution. Whereas SD is appropriate for local (e.g., gauge) resolution, BA may be a good alternative when the gap between the predictor and predictand resolution is small. However, the different sources of uncertainty affecting SD such as reanalysis uncertainty, the choice of suitable predictors, climate model, and/or statistical approach may yield implausible projections in particular situations for which BA techniques may offer a compromise alternative, even for local resolution. In this work, we consider a case study with 41 rain gauges over Malawi and show that, despite producing similar results for a historical period, the use of different predictors may lead to large differences in the future projections obtained from SD methods. For instance, using temperature T (specific humidity Q) produces much drier (wetter) conditions than those projected by the raw global models for the target area. We demonstrate that this can be partially alleviated by substituting T+Q by relative humidity R, which simultaneously accounts for both water availability and temperature, and yields regional projections more compatible with the global one. Nevertheless, large local differences still persist, lacking a physical interpretation. In these situations, the use of simpler approaches such as empirical BA may lead to more plausible (i.e., more consistent with the global model) projections.

中文翻译：

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统计缩减或偏差调整？一个案例研究涉及对马拉维降水的不可信的气候变化预测

统计降尺度 (SD) 和偏差调整 (BA) 方法通常用于从粗略的全球模型输出中生成区域到当地的气候变化预测。这些技术的适用性取决于感兴趣的特定应用，尤其是所需的空间分辨率。虽然 SD 适用于局部（例如，仪表）分辨率，但当预测变量与预测变量和分辨率之间的差距很小时，BA 可能是一个很好的选择。然而，影响 SD 的不同不确定性来源，例如再分析不确定性、合适预测因子的选择、气候模型和/或统计方法，可能会在特定情况下产生不可信的预测，而 BA 技术可能会为此提供折衷的替代方案，即使对于局部分辨率也是如此。在这项工作中，我们考虑在马拉维使用 41 个雨量计的案例研究，并表明，尽管在历史时期产生了相似的结果，但使用不同的预测因子可能会导致从 SD 方法获得的未来预测的巨大差异。例如，使用温度 T（比湿度 Q）会产生比目标区域的原始全球模型预测的条件更干燥（更潮湿）的条件。我们证明，这可以通过用相对湿度 R 代替 T+Q 来部分缓解，这同时考虑了可用水量和温度，并产生与全球预测更相符的区域预测。尽管如此，巨大的局部差异仍然存在，缺乏物理解释。在这些情况下，使用更简单的方法如经验 BA 可能会导致更合理的（即，