Climate change impact studies commonly use impact models (such as hydrological or crop models) forced with corrected climate input data from global climate models (GCMs). A range of downscaling and bias correction methods have been developed to increase the spatial resolution and remove systematic biases in climate model outputs to be applied before use in impact models. Many studies have focused on evaluating such approaches for the climate variables they aim to correct. However, due to nonlinear error propagation there can be large remaining biases in model outputs, even when ingesting bias corrected climate forcings.

Here we propose an impact-centric evaluation framework for downscaling and bias correction methods to be used in climate change risk assessments. This framework evaluates and compares the strengths and limitations of downscaling and bias correction in the impact domain, highlighting approaches that lead to reduced biases in impacts on variables of interest. We demonstrate the evaluation framework in the context of assessing downscaling and bias correction methods for hydrological projections in Australia. Our results show that although all downscaling and bias correction methods evaluated perform adequately for the input climate variables, their errors vary markedly when the impact is modelled. Our proposed evaluation framework involves selecting a number of key performance metrics, and ranking the four downscaling and bias correction methods to compute an overall ranking, highlighting the best-performing methods for each statistical metric and the overall best-performing approach. We present an application of this approach using performance metrics relevant to hydrological applications, relating to mean biases, variability, heavy precipitation and peak runoff days, and dry conditions.

For impact studies related to hydrological applications, we find that multi-variate bias correction that considers cross-correlations, temporal auto-correlations and biases at multiple time scales (daily to annual) performs best in reducing biases in hydrological output variables for Australia. Our proposed evaluation approach can be applied to a wide range of climate change applications where downscaling and bias correction are required, including impacts on agricultural production, wildfires, energy generation, human health, ecosystem functioning, and water resource management.

气候变化影响研究通常使用影响模型（如水文或作物模型），强制使用来自全球气候模型 (GCM) 的校正气候输入数据。已经开发了一系列降尺度和偏差校正方法，以提高空间分辨率并消除气候模型输出中的系统偏差，以便在用于影响模型之前应用。许多研究都侧重于评估这些方法对它们旨在纠正的气候变量的影响。然而，由于非线性误差传播，模型输出中可能存在较大的剩余偏差，即使在摄取偏差校正的气候强迫时也是如此。

在这里，我们提出了一个以影响为中心的评估框架，用于气候变化风险评估中使用的降尺度和偏差校正方法。该框架评估和比较了影响域中降尺度和偏差校正的优势和局限性，突出了导致减少对感兴趣变量的影响偏差的方法。我们在评估澳大利亚水文预测的降尺度和偏差校正方法的背景下展示了评估框架。我们的结果表明，尽管评估的所有降尺度和偏差校正方法对输入气候变量都表现得很好，但在对影响进行建模时，它们的误差会发生显着变化。我们提出的评估框架涉及选择一些关键绩效指标，并对四种降尺度和偏差校正方法进行排名以计算总体排名，突出显示每个统计指标的最佳性能方法和总体最佳性能方法。我们使用与水文应用相关的性能指标展示了这种方法的应用，这些指标涉及平均偏差、变异性、强降水和峰值径流天数以及干旱条件。

对于与水文应用相关的影响研究，我们发现考虑互相关、时间自相关和多个时间尺度（每日到年度）偏差的多变量偏差校正在减少澳大利亚水文输出变量偏差方面表现最佳。我们提出的评估方法可广泛应用于需要降尺度和偏差校正的气候变化应用，包括对农业生产、野火、能源生产、人类健康、生态系统功能和水资源管理的影响。