Summary We present the application of a parametric stochastic weather generator within a nonstationary context, enabling simulations of weather sequences conditioned on interannual and multi-decadal trends. The generalized linear model framework of the weather generator allows any number of covariates to be included, such as large-scale climate indices, local climate information, seasonal precipitation and temperature, among others. Here we focus on the Salado A basin of the Argentine Pampas as a case study, but the methodology is portable to any region. We include domain-averaged (e.g., areal) seasonal total precipitation and mean maximum and minimum temperatures as covariates for conditional simulation. Areal covariates are motivated by a principal component analysis that indicates the seasonal spatial average is the dominant mode of variability across the domain. We find this modification to be effective in capturing the nonstationarity prevalent in interseasonal precipitation and temperature data. We further illustrate the ability of this weather generator to act as a spatiotemporal downscaler of seasonal forecasts and multidecadal projections, both of which are generally of coarse resolution.

中文翻译：

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用于季节性到多年代际模拟的条件随机天气发生器

总结 我们介绍了参数随机天气发生器在非平稳环境中的应用，能够模拟以年际和多年代际趋势为条件的天气序列。天气发生器的广义线性模型框架允许包含任意数量的协变量，例如大尺度气候指数、局部气候信息、季节性降水和温度等。在这里，我们将重点放在阿根廷潘帕斯草原的 Salado A 盆地作为案例研究，但该方法可移植到任何地区。我们包括域平均（例如，区域）季节性总降水量和平均最高和最低温度作为条件模拟的协变量。区域协变量由主成分分析驱动，该分析表明季节性空间平均值是整个域的主要变异模式。我们发现这种修改可以有效地捕捉季节间降水和温度数据中普遍存在的非平稳性。我们进一步说明了这个天气发生器作为季节性预测和多年代际预测的时空缩减器的能力，这两种预测通常都是粗略的分辨率。