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There are many environmental challenges in water-limited places in the 21st century, particularly in dry and semi-arid regions, due to the threat of climate change caused by the greenhouse effect. This study intends to explore and assess the influence of climate change on hydro-climatological parameters using statistical downscaling and future forecasts of mean monthly precipitation and temperature throughout Famagusta (Mağusa), Nicosia (Lefkoşa), and Kyrenia (Girne) stations, North Cyprus. To achieve the study's goal, 13 predictors of BNU-ESM GCMs from CMIP5 were used at a grid point in the Karfas region. To find the primary predictors, GCM data were screened using mutual information (MI) and correlation coefficient (CC) feature extraction methods prior to downscaling modeling. A neural network (ANN), an adaptive neuro fuzzy inference system (ANFIS), and multiple linear regression (MLR) models were employed as the downscaling models. We used the best downscaling model as a benchmark for future precipitation and temperature estimates for the period 2018–2040 under the RCP4.5 scenario. In the future, Famagusta and Nicosia would have up to 22% less rain, and Famagusta and Kyrenia will have 2.9% greater heat. The findings of this research could be useful in decision-making, as well as water resource management and climate change.

journal of hydroinformatics

Impact of climate change on hydro-climatological parameters in North Cyprus: application of artificial intelligence-based statistical downscaling models