Assessment of rainfed wheat productivity in a changing climate in Irbid, Jordan using statistical downscaling and Random Forest Regression prediction under RCP4.5 & 8.5 pathways

Jordan is confronted with substantial risks linked to climate change and is proactively striving to manage resources sustainably by aligning its initiatives with the Sustainable Development Goals (SDGs). This study examines the impacts of climate change on rainfed wheat productivity in Irbid Governorate during the historical period (1994–2021) and simulated for the future (2024–2100) using climate models (SDGs 1, 2, 11, 12, 13, & 15). Monthly precipitation, mean temperature, and annual wheat yield data were collected for the period (1994–2021) and analyzed using the Mann-Kendall test with Sen's slope to investigate the historical trends and elaborate Phi-k correlation coefficient to determine their association. Monthly precipitation and mean temperature projections were collected from CSIRO-MK3.6.0 & GFDL-ESM2M CMIP5 ensemble models under two concentration pathways: RCP4.5 and RCP8.5. The projections were biased and corrected by the dynamic bilinear interpolation method and Delta, EQM, and QM statistical downscaling to enhance the projections' reliability and performance in capturing the region's climate. Taylor diagram was utilized to choose the best model to represent observed data. Cumulative Distribution Function and Probability Density Function Curves were plotted to describe the changes over decades based on climate models. Future wheat yields were predicted using the Random Forest Regression model. The results revealed a non-significant increase in total annual precipitation of 1.8% and 13.8% and a rise in annual mean temperature of 5.4% and 2.7% for Irbid and Baqura stations, respectively during the baseline timeframe on which wheat yield increased by 21.3%. The CSIRO model outperformed the GFDL model with greater fidelity in simulating historical monthly precipitation and mean temperatures. The CSIRO-MK3.6.0 model indicates precipitation and temperature shifts for near and far future periods. Temperature increases are expected to have more severe impacts in the far future (2073–2100) while projected decreases in precipitation of 0.06 mm/day and 0.10 mm/day under RCP4.5 and RCP8.5 affect yield reductions by 11.42 kg/ha and 12.21 kg/ha. Temperature increases of 2.7 °C and 4.1 °C under RCP 4.5 & 8.5 and the responding yield decreases to 24.83 kg/ha and 33.40 kg/ha, respectively. The study highlights the need for enhanced adaptation strategies; cultivating resilient wheat varieties, promoting crop rotation, improving meteorological monitoring and risk response approaches, and providing timely weather forecasts to mitigate the adverse effects of climate change on wheat productivity and ensure sustainable agriculture in Jordan. • Wheat-rainfed areas respond to higher temperatures and low precipitation intensity depending on the emissions pathway. • Statistical downscaling methods debiased the projection enhancing its reliability in capturing the baseline climate. • Empirical quantile mapping works better in correcting the temperature and precipitation projections in near and far futures. • Forest regression shows a promising model predicting wheat yield with high accuracy and better performance.