SDSM

SDSM, or Stochastic Downscaling Simulations, is a method used to make large-scale climate or weather predictions more relevant for local areas. It takes broad, general climate model data and adds detailed variations to simulate specific local conditions, like rainfall or temperature, more accurately. By using statistical techniques, SDSM captures the natural randomness and variability of weather patterns, helping scientists and planners better understand and prepare for regional climate impacts, such as droughts or floods. Essentially, it refines big-picture climate data into more precise, location-specific information for better decision-making.