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

TL;DR

This paper introduces a stochastic downscaling method combining ERA5 reanalysis data with local observations to generate realistic local daily temperature and precipitation series, improving accuracy over large European regions.

Contribution

It presents a novel non-linear regression framework integrating GAMs, splines, and ARMA models for efficient downscaling of ERA5 data to local scales.

Findings

Improved local temperature and precipitation representation compared to ERA5.

Validated across 4000 European locations over 60 years.

Enhanced realism of local climate series for research and applications.

Abstract

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.