Because all climate models exhibit biases, their use for assessing future climate change requires implicitly assuming or explicitly postulating that the biases are stationary or vary predictably. This hypothesis, however, has not been, and cannot be, tested directly. This work shows that under very large climate change the bias patterns of key climate variables exhibit a striking degree of stationarity. Using only correlation with a model’s preindustrial bias pattern, a model’s 4xCO₂ bias pattern is objectively and correctly identified among a large model ensemble in almost all cases. This outcome would be exceedingly improbable if bias patterns were independent of climate state. A similar result is also found for bias patterns in two historical periods. This provides compelling and heretofore missing justification for using such models to quantify climate perturbation patterns and for selecting well-performing models for regional downscaling. Furthermore, it opens the way to extending bias corrections to perturbed states, substantially broadening the range of justified applications of climate models.

由于所有气候模型都存在偏差，因此将其用于评估未来的气候变化需要隐式假设或明确假设偏差是平稳的或可预测的。但是，这一假设尚未被，也无法被直接检验。这项工作表明，在非常大的气候变化下，关键气候变量的偏差模式表现出惊人的平稳性。仅使用与模型的工业前偏差模式的相关性，模型的4xCO ₂在几乎所有情况下，都能在大型模型集合中客观，正确地识别偏差模式。如果偏见模式与气候状态无关，那么这种结果将是极不可能的。对于两个历史时期的偏差模式也发现了类似的结果。这为使用此类模型量化气候扰动模式以及为区域缩小规模选择性能良好的模型提供了令人信服的，迄今仍缺乏的论据。此外，它为将偏差校正扩展到扰动状态开辟了道路，从而大大拓宽了气候模型合理应用的范围。