Evaluation of different downscaling approaches for very-high-resolution climate data

Climate change leads to widespread changes in atmospheric and oceanic conditions, increasing the frequency of climate anomalies and negatively impacting ecosystems and human communities. It is crucial to understand climate change and make accurate predictions about it. Climate change studies focus on tools like General Circulation Models (GCMs); however, GCMs cannot accurately represent local climates, leading to uncertainties due to their coarse resolution. Statistical and dynamical downscaling techniques improve local climate projection accuracy. This study compared statistical and dynamical downscaling techniques for evaluating Turkey's climate change projections, using the MPI-ESM-MR as the main GCM, RegCM4.7.0 regional climate model for dynamical downscaling and the spatial delta method for statistical downscaling. 17 datasets were analyzed to investigate spatio-temporal correlations at resolutions of 1km, 5km, 10km, and 20km. Evaluated spatial correlation of precipitation and temperature showed low to moderate correlation coefficients with negative correlations and near-zero values for precipitation but higher correlation results for temperature. 10 and 20km resolution downscaling data showed more favorable results. The temporal correlation of precipitation showed superior consistency with reduced standard deviations and improved correlation coefficients. The study highlighted the temporal correlation of temperature, exhibiting exceptional precision due to its nature and alignment with annual seasonal cycles. This study's findings will significantly enhance understanding of the optimal methodology for downscaling climate change projections and the impacts of climate change on local communities.