Title: High-resolution seasonal climate prediction with stepwise cluster analysis: a case study for Prince Edward Island, Canada
Journal: Climate Dynamics
DOI: https://doi.org/10.1007/s00382-025-07663-2
Abstract: Global warming has become a pervasive force, impacting environments and livelihoods around the globe. Accurate and high-resolution seasonal climate predictions have become a critical aspect of climate research, providing valuable insights for agriculture, tourism, fisheries, and disaster preparedness sectors. This study introduces a stepwise cluster analysis (SCA) approach for predicting seasonal climate conditions for temperature and precipitation at a spatial resolution of 1 km × 1 km for the province of Prince Edward Island (PEI), Canada. Specifically, the Canadian Seasonal and Interannual Prediction System (CanSIPS) dataset on 1° resolution (~ 100 km) is first downscaled to 1 km with the SCA approach. The robust empirical quantile mapping (RQUANT) method is then applied for bias correction. The results demonstrate that the proposed method can effectively capture the spatiotemporal patterns of seasonal temperature and precipitation across PEI, as indicated by observational data. The SCA approach introduced in this research can be used to support the development of informed decision-making processes and proactive measures in response to seasonal climate variations in the region.
CLISA is the first multi-institutional training program in Canada towards climate smart agriculture to help address the need for HQPs who possess appropriate knowledge and expertise in climate change, precision agriculture, water and soil management, sustainable food production and food value chains, and climate-smart financing and policies to promote the development and application of innovative technologies and strategies in Canadian farming practices.
