The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC-2). The main objective of this article is to establish a comparison among five statistical downscaling methods developed at AEMET: (1) Analog, (2) Regression, (3) Artificial Neural Networks, (4) Support Vector Machines and (5) Kernel Ridge Regression. This comparison has been carried out under present conditions and with perfect predictors, based on the framework established by the VALUE network, in particular, on its perfect predictor experiment. In this experiment, we evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation analysed by seasons, on a high resolution observational grid (0.05°) over mainland Spain and the Balearic Islands. This is the first of a set of three experiments aimed to allow us to decide which methods, and under what configuration, is more appropriate for the generation of downscaled climate projections over our region. For maximum/minimum temperatures, all methods display a similar behaviour. They capture very satisfactorily the mean values although slight biases are detected on the extremes. In general, results for maximum temperature appear to be more accurate than for minimum temperature, and the nonlinear methods display certain added value. For precipitation, remarkable differences are found among all methods. Most of the methods are capable of reproducing the total precipitation amount quite satisfactorily, whereas other aspects such as intense precipitations and the precipitation occurrence are captured with more accuracy by the Analog method.

中文翻译：

---

西班牙气候变化预测的统计降尺度方法评估：具有完美预测因子的现状

西班牙气象局 (AEMET) 负责制定西班牙上空的缩减气候预测，以支持第二个国家适应气候变化计划 (PNACC-2)。本文的主要目的是比较 AEMET 开发的五种统计降尺度方法：（1）模拟，（2）回归，（3）人工神经网络，（4）支持向量机和（5）核脊回归. 这种比较是在当前条件下，基于 VALUE 网络建立的框架，使用完美的预测器进行的，特别是在其完美的预测器实验上。在本实验中，我们在高分辨率观测网格 (0. 05°）在西班牙大陆和巴利阿里群岛上空。这是一组三个实验中的第一个，旨在让我们决定哪些方法以及在什么配置下更适合生成我们地区的缩小气候预测。对于最高/最低温度，所有方法都显示类似的行为。尽管在极端情况下检测到轻微的偏差，但它们非常令人满意地捕获了平均值。一般来说，最高温度的结果似乎比最低温度的结果更准确，并且非线性方法显示出一定的附加值。对于降水，发现所有方法之间存在显着差异。大多数方法都能够相当令人满意地再现总降水量，