Enhancing Climate Forecast Precision in Specified Nigerian Regions Through Statistical Downscaling and Bias Correction Techniques

Abstract

The focus of this research is to assess the effectiveness of statistical downscaling and bias correction methods in predicting future climate conditions under various climate change scenarios in Benin City, Enugu, Lokoja, and Port Harcourt. The study utilizes 14 years of daily precipitation data spanning from 1982 to 1995 obtained from four Food and Agricultural Organization (FAO) climate change meteorological stations. Simulated input data from Regional Circulation Models (RCMs) was acquired from The Earth System Grid Federation (ESGF) online platform. Daily precipitation data for the future period (2041–2050) from RCMs (AFR-44) was employed. The data underwent bias correction and statistical downscaling with a spatial resolution of 0.35 degrees. Analysis of the RCM-simulated historical data reveals intense precipitation activity, particularly in the Benin region and Port Harcourt city. However, a comparison with observed data from meteorological stations highlights significant discrepancies, underscoring the necessity of bias correction and downscaling techniques before utilizing such data for environmental analyses.