Intercomparison of machine learning methods for statistical downscaling of daily temperature under CMIP6 scenarios: a case study from Iran

Abstract

ABSTRACT General circulation models (GCM) represent a contemporary and advanced tool designed to simulate the response of climate system to alterations in greenhouse gas levels. Increasing spatial resolutions of the outputs of GCMs on a regional scale requires downscaling process. In this study, four machine learning (ML) models, namely, decision tree regression (DTR), support vector regression, artificial neural network, and K-nearest neighbors (KNN) were employed to downscale the outputs of four Coupled Model Intercomparison Project 6 climate models. The daily observations of temperature data at Nazmakan station in Kohgiluyeh and Boyer-Ahmad Province, Iran, were trained and tested for the periods 1995–2009 and 2009–2015, respectively. According to results of four metrics, DTR and KNN obtained the most valid evaluation in the training and test data, respectively. In addition, downscaling methods were used to predict future daily temperature for 2015–2045 under two Shared Socioeconomic Pathway (SSP2-4.5 and SSP5-8.5) scenarios. Mann–Kendall test results revealed an increasing trend of temperature in most of the coming years. The projected trend expects cooler summers and warmer winters in future. Furthermore, the predicted data under two climate scenarios were assessed in comparison with observed data for 2015–2022. Finally, this study demonstrates the strengths and weaknesses of nonlinear ML techniques in statistical downscaling.