Regional Climate Models (RCM) describe the meso scale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. In other words, RCMs use atmospheric and oceanic climate output from General Circulation Models (GCM) to develop a higher resolution climate output. They are computationally demanding and, depending on the application, require several orders of magnitude of computer time more than statistical climate downscaling. In this paper we describe how to use a spatio-temporal statistical model with varying coefficients (VC), as a downscaling emulator for a RCM using varying coefficients. In order to estimate the proposed model, two options are compared: INLA, and varycoef. We set up a simulation to compare the performance of both methods for building a statistical downscaling emulator for RCM, and then show that the emulator works properly for NARCCAP data. The results show that the model is able to estimate non-stationary marginal effects, which means that the downscaling output can vary over space. Furthermore, the model has flexibility to estimate the mean of any variable in space and time, and has good prediction results. INLA was the fastest method for all the cases, and the approximation with best accuracy to estimate the different parameters from the model and the posterior distribution of the response variable.

翻译：区域气候模型（RCM）描述中尺度全球大气和海洋动力学，并作为动力降尺度模型运行。换言之，RCM利用全球环流模型（GCM）的大气和海洋气候输出来生成更高分辨率的气候输出。其计算需求高，且根据应用场景，所需计算机时间比统计气候降尺度高出数个数量级。本文描述了如何使用带变系数（VC）的时空统计模型作为基于变系数的RCM降尺度仿真器。为了估计所提出的模型，比较了两种方案：INLA和varycoef。我们设计了一项模拟实验来比较这两种方法在构建RCM统计降尺度仿真器时的性能，并证明该仿真器能有效处理NARCCAP数据。结果表明，该模型能够估计非平稳边际效应，即降尺度输出可随空间变化。此外，该模型能灵活估计任意变量在空间和时间上的均值，并具有良好的预测结果。在所有案例中，INLA是最快的方法，且对模型不同参数及响应变量后验分布的估计精度最高。