Research Projects

The increasing impact of climate change necessitates accurate regional projections of climate extremes such as heatwaves and extreme rainfall events. However, these projections are highly sensitive to the observational datasets used for bias correction as well as the choice of downscaling methods. This research proposes to develop a comprehensive and systematic framework to address uncertainties arising from observational and methodological choices during the downscaling process. The core hypothesis is that integrating multiple observational datasets and diverse downscaling methodologies, including artificial intelligence techniques, will provide a more robust and credible projection of extreme climatic events. The project will test various combinations of bias correction methods and downscaling techniques using CMIP6 GCM outputs. Projected heat and rainfall extremes will be evaluated through standard indices. Observational uncertainty will be addressed by using multiple reference datasets. If successful, the proposed framework will contribute significantly to climate science by improving the reliability of regional extreme event projections and informing adaptation strategies for climate-sensitive sectors such as agriculture, urban planning, and disaster risk management.