General circulation models (GCMs) are essential tools for understanding climate behavior and projecting future global climate, but with limited applications for local vulnerability assessments, impact studies, and risk analyses. This study demonstrates the use of statistical downscaling technique, which is computationally inexpensive and efficient in generating locally relevant data from GCMs. The 30-year records of daily precipitation data from 1981 to 2010 in Agusan del Norte, Philippines were considered to analyze the existing climatic condition of the province. Future precipitation magnitude, centered on 2020 (2006-2035), 2050 (2036-2065), and 2080 (2066-2095), were generated using a statistical downscaling technique under A1B climate scenario. Based on the analyses of annual 24-hour and 48-hour maximum precipitation, climatic conditions for present and future scenarios were analyzed. Precipitations with 24-hour duration are expected to increase by about 2-18%, 6-19%, and 15-51% for the period 2020, 2050, and 2080, respectively. Meanwhile, rainfall with 48-hour duration is projected to increase by about 7-17%, 7-18%, and 16-51% for the period 2020, 2050, and 2080, respectively. Future precipitation scenarios can help stakeholders from different sectors in the province evaluate impacts and identify tradeoffs towards implementing the most appropriate climate change adaptation strategies.
