A Framework for the Assessment of Rainfall Disaggregation Methods in Representing Extreme Precipitation

Abstract. High-resolution precipitation data are essential to analyze extreme rainfall, critical for hydrological modeling, infrastructure design and climate change assessments. As high-resolution rainfall data are limited, disaggregation methods become an alternative to access such data. Although many studies have evaluated these methods, there is no framework for their selection based on their performance in reproducing extreme attributes. This paper presents a framework for evaluating daily-to-hourly rainfall disaggregation methods, measuring the performance on representing extreme precipitation behavior. The framework assesses this performance using Intensity-Duration-Frequency (IDF) curves and extreme rainfall indices (ERIs). IDF curve disaggregation performance evaluation uses accuracy and precision metrics (i.e., how close and consistent disaggregated values are to observed data, respectively), while ERIs are assessed by comparing the variability and bias of disaggregated annual series to observed data using a modified Kling-Gupta efficiency. The framework was applied to five sites with diverse climates, using three disaggregation methods: (1) a stochastic pulse-type method (SOC), (2) a non-parametric k-nearest neighbor (k-NN), and (3) a method based on Huff curves (HUFF). Results show that k-NN tends to outperform other methods in replicating IDF curves, modeling extreme rainfall percentiles and capturing the occurrence and magnitude of intense precipitation events, as well as most critical dry situations. SOC performs well in precision but has a lower ability in accuracy while HUFF is best at modeling 5-hour maximum rainfall. Nonetheless, these performances are not consistent across all locations, with the best-performing method varying per site, highlighting the importance of context-specific evaluations enabled by the framework.