High Resolution Forecasting of Summer Drought in the Western United States

These data support the findings presented in the the published manuscript Abolafia-Rosenzweig et al. (2023), "High Resolution Forecasting of Summer Drought in the Western United States" (Water Resources Research, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022WR033734)

Abstract
Drought monitoring and forecasting systems are used in the United States (U.S.) to inform drought management decisions. Drought forecasting efforts have often been conducted and evaluated at coarse spatial resolutions (i.e., >10-km), which miss key local drought information at higher resolutions. Addressing the importance of forecasting drought at high resolutions, this study develops statistical models to evaluate 1- to 3-month lead time predictability of meteorological and agricultural summer drought across the western U.S. at a 4-km resolution. Our high-resolution drought predictions have statistically significant skill (p ≤ 0.05) across 70%–100% of the western U.S., varying by evaluation metric and lead time. 1- to 3-month lead time drought forecasts accurately represent monitored summer drought spatial patterns during major drought events, the interannual variability of drought area from 1982 to 2020 (r = 0.84–0.93), and drought trends (r = 0.94–0.97). 71% of western U.S summer drought area interannual variability can be explained by cold-season (November–February) climate conditions alone allowing skillful 3-month lead time predictions. Pre-summer drought conditions (represented by drought indices) are the most important predictors for summer drought. Thus, the statistical models developed in this study heavily rely on the autocorrelation of chosen agricultural and meteorological drought indices which estimate land surface moisture memory. Indeed, prediction skill strongly correlates with persistence of drought conditions (r ≥ 0.73). This study is intended to support future development of operational drought early warning systems that inform drought management.