This study aims to enhance the accuracy and reliability of the Global Ensemble Forecast System’s (GEFS) precipitation forecasts over the Indian subcontinent using two post-processing techniques, namely the Analog method (AN) and Logistic Regression method (LR). The post-processing techniques and GEFS Numerical Weather Prediction Model (NWP) outputs were evaluated against the observed dataset using probabilistic and deterministic evaluation metrics. Results found that both the methods considerably improves short range to medium range (1-15 day) precipitation forecasts over India. Overall results showed that both the methods perform poorly during the monsoon seasons compared to other seasons. Basin analysis showed that both the methods underperform in the Western Ghats, while the performance is comparable and decent in other parts of India. Analysis of precipitation at different terciles showed that both the AN and LR methods underperforms at higher terciles compared to the lower ones. This is because the GEFS model itself was performing poorly in detecting the heavy precipitation events. The comparison of logistic regression and analog methods shows that the LR method outperforms the AN method in almost all the locations and lead times.

Flash droughts are a recently recognized extreme climate phenomena that occur at the subseasonal timescale and develop with sudden onset and rapid intensity, which have significant socio-environmental impacts on agriculture, ecosystem, and water resources. However, the spatial and temporal variability of flash droughts in the continental United States (CONUS) are still not well understood. In this study, we characterize flash drought events using a novel evaporative demand flash drought (EDFD) index as well as a soil moisture flash drought (SMFD) index at the weekly timescale between September 1981 and December 2018. Hierarchical clustering divided CONUS into different clusters where SMFDs occur synchronously over space and time. The results show that flash droughts occur in all seasons and regions in CONUS with increasing trends in EDFDs (0.07 > tau > 0.28), but not equal trends in SMFDs (-0.12 > tau > 0.08). Periodic short-term behavior between EDFDs and SMPDs was identified in all clusters but more importantly long-term oscillatory behavior is identified between the two indexes in all clusters except the Northwest US. Our research into the spatiotemporal variability of flash droughts using both the EDFD index and SMPD index displays that CONUS is still at risk for increased flash drought events in the future to come.