Despite increasing evidence of intensification of extreme precipitation events associated with a warming climate, the magnitude of extreme river flows is decreasing in many parts of the world. To better understand the range of relationships between precipitation extremes and floods, we analyzed annual precipitation extremes and flood events over the Contiguous United States from 1980 to 2014. A low spatial correlation (less than 0.2) between changes in precipitation extremes and changes in floods was found, attributable to a weak causal relationship. The co-variation between precipitation extremes and floods is also substantially low, with a majority of catchments having a coefficient of determination of less than 0.5, even among the catchments with a relatively strong causal relationship. The findings indicate a need for more investigations into causal mechanisms driving a non-linear response of floods to intensified precipitation extremes in a warming climate.

We analyzed streamflow records from more than 300 stations across Thailand, a tropical country in Southeast Asia. Temporal changes in runoff yield were assessed over the 1960-2015 period, highlighting a prominent downward trend over the last two decades. To identify potential drivers of these changes, gridded data products representing precipitation, temperature, evapotranspiration, and land cover were also assessed. We found that runoff yield is primarily driven by annual precipitation, which has experienced an unprecedented decline since 2010. Two sub-regions with particularly robust data coverage reflected a spatial contrast in hydrologic response: a more consistent response of runoff yield to precipitation is observed in the sub-region characterized by a high density of forest cover relative to the region characterized by high cropland cover. This feature underscores the need to take land use and irrigation practices into account when forecasting, and determining management strategies for, tropical river streamflow in a warming climate.

The accurate representation of the local-scale variability of precipitation plays an important role in understanding the hydrological cycle and land-atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper-resolution precipitation data is of urgent need. In this study, we propose a statistical framework to downscale the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) precipitation product using the random forest classification and regression algorithm. A set of variables representing atmospheric, geographic, and vegetation cover information are selected as model predictors, based on a recursive feature elimination method. The downscaled precipitation product is validated in terms of magnitude and variability against a set of ground- and satellite-based observations. Results suggest improvements with respect to the original resolution MERRA-2 precipitation product and comparable performance with gauge-adjusted satellite precipitation products.

This first paper of the two-part series focuses on demonstrating the predictability of a hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01 degree (∼ 1-km) and 0.25 degree (∼ 25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01 degree estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse-resolution estimates. The model forced by downscaled forcings in its entirety yields the highest predictability skill in model output states as well as precipitation, which improves the skill obtained by coarse-resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper-resolution versus coarse-resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper-resolution precipitation products to drive model simulations.