Climate models exhibit errors in their simulation of historical trends of variables including sea surface temperature, winds, and precipitation, with important implications for regional and global climate projections. Here, we show that the same trend errors are also present in a suite of initialised seasonal re-forecasts for the years 1993–2016. These re-forecasts are produced by operational models that are similar to Coupled Model Intercomparison Project (CMIP)-class models and share their historical external forcings (e.g. CO₂/aerosols). The trend errors, which are often well-developed at very short lead times, represent a roughly linear change in the model mean biases over the 1993–2016 re-forecast record. The similarity of trend errors in both the re-forecasts and historical simulations suggests that climate model trend errors likewise result from evolving mean biases, responding to changing external radiative forcings, instead of being an erroneous long-term response to external forcing. Therefore, these trend errors may be investigated by examining their short-lead development in initialised seasonal forecasts/re-forecasts, which we suggest should also be made by all CMIP models.

气候模型在模拟包括海面温度、风和降水在内的变量的历史趋势时表现出错误，对区域和全球气候预测具有重要影响。在这里，我们表明，相同的趋势误差也存在于 1993-2016 年的一组初始季节性重新预测中。这些重新预报是由类似于耦合模型比较计划 （CMIP） 类模型的业务模型生成的，并共享它们的历史外部强迫（例如 CO₂/气溶胶）。趋势误差通常在非常短的提前期内得到充分发展，代表了 1993-2016 年重新预测记录中模型均值偏差的大致线性变化。再预报和历史模拟中趋势误差的相似性表明，气候模式趋势误差同样是由不断变化的平均偏差引起的，响应不断变化的外部辐射强迫，而不是对外部强迫的错误长期响应。因此，可以通过检查它们在初始季节性预测/再预测中的短前发展来研究这些趋势误差，我们建议所有 CMIP 模型也应该这样做。