标题： 局部日温度和降水序列的模拟与评估，由 ERA5 再分析数据的随机降尺度得到 显示英文标题

标题： Simulation and evaluation of local daily temperature and precipitation series derived by stochastic downscaling of ERA5 reanalysis

摘要： 再分析产品，如ERA5再分析数据，通常用作观测大气条件的替代品。 这些产品由于其全球覆盖范围、大量可用的大气变量以及这些变量之间的物理一致性，以及相对较高的空间和时间分辨率，使用起来非常方便。 然而，尽管再分析产品的准确性和空间及时间分辨率不断提高，它们可能并不总是能捕捉到局部的大气条件，特别是对于像降水这样高度局域化的变量。 本文提出了一种计算效率高的ERA5温度和降水的随机降尺度方法。 该方法在非线性回归框架中结合了来自ERA5和附近站点地表观测的信息，该框架将广义可加模型（GAMs）与回归样条和自回归移动平均（ARMA）模型相结合，以生成局部每日温度和降水的真实时间序列。 使用一系列评估标准，这些标准涉及数据的不同特性，所提出的框架被证明在欧洲超过4000个地点的60年期间，相比ERA5提高了局部温度和降水的表示效果。 显示英文摘要

摘要： Reanalysis products such as the ERA5 reanalysis are commonly used as proxies for observed atmospheric conditions. These products are convenient to use due to their global coverage, the large number of available atmospheric variables and the physical consistency between these variables, as well as their relatively high spatial and temporal resolutions. However, despite the continuous improvements in accuracy and increasing spatial and temporal resolutions of reanalysis products, they may not always capture local atmospheric conditions, especially for highly localised variables such as precipitation. This paper proposes a computationally efficient stochastic downscaling of ERA5 temperature and precipitation. The method combines information from ERA5 and surface observations from nearby stations in a non-linear regression framework that combines generalised additive models (GAMs) with regression splines and auto-regressive moving average (ARMA) models to produce realistic time series of local daily temperature and precipitation. Using a wide range of evaluation criteria that address different properties of the data, the proposed framework is shown to improve the representation of local temperature and precipitation compared to ERA5 at over 4000 locations in Europe over a period of 60 years.