We compare historical and end‐of‐century temperature and precipitation patterns over California from one dynamically downscaled simulation using the Weather Research and Forecast (WRF) model and two simulations statistically downscaled using Localized Constructed Analogs (LOCA). We uniquely separate causes of differences between dynamically and statistically based future climate projections into differences in historical climate (gridded observations versus regional climate model output) and differences in how these downscaling techniques explicitly handle future climate changes (numerical modeling versus analogs). In these methods, solutions between different downscaling techniques differ more in the future compared to the historical period. Changes projected by LOCA are insensitive to the choice of driving data. Only through dynamical downscaling can we simulate physically consistent regional springtime warming patterns across the Sierra Nevada, while the statistical simulations inherit an unphysical signal from their parent Global Climate Model (GCM) or gridded data. The results of our study clarify why these different techniques produce different outcomes and may also provide guidance on which downscaled products to use for certain impact analyses in California and perhaps other Mediterranean regimes.

中文翻译：

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了解由动态和统计缩减产生的加利福尼亚气候预测差异

我们比较了使用天气研究和预报（WRF）模型进行的一种动态缩减模拟和使用本地化的构建模拟量（LOCA）进行了统计缩减的两种模拟，从而比较了加利福尼亚州的历史和本世纪末的温度和降水模式。我们将基于动态和统计的未来气候预测之间差异的原因独特地分为历史气候差异（观测数据与区域气候模型输出）以及这些降尺度技术如何显式处理未来气候变化的差异（数值模拟与类似物）。在这些方法中，与历史时期相比，未来不同缩水技术之间的解决方案差异更大。LOCA预测的更改对驾驶数据的选择不敏感。只有通过动态降尺度，我们才能在内华达山脉上模拟物理上一致的区域春季变暖模式，而统计模拟则从其父级全球气候模型（GCM）或网格数据继承非物理信号。我们的研究结果阐明了为什么这些不同的技术会产生不同的结果，并且还可能为在加利福尼亚甚至其他地中海地区的某些影响分析中使用哪种缩减规模的产品提供指导。