Temporal downscaling of precipitation from climate model projections using machine learning

Increased greenhouse gas concentration in the atmosphere has led to significant climate warming and changes in precipitation and temperature characteristics. These trends, which are expected to continue, will affect water infrastructure and raise the need to update associated planning and design policies. The potential effects of climate change can be addressed, in part, by incorporating outputs of climate model projections into statistical assessments to develop the Intensity Duration Frequency (IDF) curves used in engineering design and analysis. The results of climate model projections are available at fixed temporal and spatial resolutions. Model results often need to be downscaled from a coarser to a finer grid spacing ( spatial downscaling) and/or from a larger to a smaller time-step ( temporal downscaling). Machine Learning (ML) models are among the methods used for spatial and temporal downscaling of climate model outputs. These methods are more frequently used for spatial downscaling; fewer studies explore temporal downscaling. In this study, multiple ML models are evaluated to temporally downscale precipitation time-series (available at 3-h time steps) generated by several regional climate models of the North American Regional Climate Change Assessment Program (NARCCAP) under a high-carbon-emission projection. The temporally downscaled time-series for 2-h, 1-h, 30-min, and 15-min durations are intended for subsequent statistical analysis to generate current- and future-climate IDF curves for Maryland. In this study, the behavior of the ML models is explored by assessing performance in predicting large target response quantities, identifying systematic trends in errors, investigating input/output relationships using response functions, and leveraging conventional performance metrics.