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The regional drought predictability is strongly influenced by teleconnections factors. Among them, El Niño-Southern Oscillation (ENSO) is important to drought occurrence. However, it is still not fully understood how the probability of regional droughts corresponding to different ENSO regimes varies with time lags. In this study, the event-based coincidence analysis (ECA) is employed to statistically derive the relationships regarding time information and magnitude between conventional ENSO (c-ENSO)/ENSO Modoki (ENSO_M) episodes and drought events. The standardized precipitation evapotranspiration index (SPEI) based on station-records is used to determine meteorological droughts. Our study focuses on the largest freshwater lake of China, Poyang Lake basin (PLB), as severe droughts have occurred there frequently. Results indicate that the summer-autumn continuous drought dominates the severe droughts, with a frequent onset in August/September, and a cessation in October/November. The likelihood of drought occurrence is highest during cool phases of c-ENSO (CEN), followed by warm ENSO_M (WEM), warm c-ENSO (WEN), and cool ENSO_M (CEM). The influence of WEN/WEM on droughts is statistically significant mostly for a time lag of 10 months, whereas CEN/CEM shows no lagged influence. Furthermore, the variability of the 850 hPa wind anomalies during different ENSO episodes at various time lags is corresponding to the lagged influence of ENSO on droughts. The development and persistence of droughts is resulting from strengthened northeasterly wind anomalies over the PLB. Our findings demonstrate that the ENSO anomalies are early warning signals of droughts and hence can be beneficial for increasing the drought predictivity • A novel statistical framework to quantify the probability of droughts responding to ENSO with varied time lags is developed. • The influence of WEN/WEM on the droughts in the PLB is statistically significant mostly for a time lag of 10 months. • The strengthened northeasterly wind anomalies over the PLB likely results in the development of ENSO-induced droughts. [ABSTRACT FROM AUTHOR]

Intense precipitation events frequently occur in the Hanjiang River Basin (HRB) in China, causing major economic losses and affecting human security. Accurate precipitation simulations are sensitive to the choice of physical schemes in numerical weather prediction models. Therefore, in this study the performance of the different combinations of physical schemes in the Weather Research and Forecasting (WRF) model was investigated for the HRB. Five intense precipitation events in summer during the period 2008–2011 were selected. For simulating each intense precipitation event, an 18-member multi-physics ensemble was designed by using three cumulus (CU) schemes and four microphysics (MP) schemes. The temporal dynamics and spatial patterns of the simulated precipitation were evaluated systematically by a comparison to the China precipitation analysis reference data. A quantitative analysis using the Euclidean distance metric to synthesize the evaluation results was performed. The performed uncertainty analysis suggests that the skill of precipitation simulation is more sensitive to the choice of the CU scheme and less sensitive to the choice of the MP scheme. Our analysis shows that a suitable WRF physical configuration consists of Kain-Fritsch and WRF Single-Moment 6-class, which reaches a performance similar to that of the ensemble mean method. To our knowledge, this multi-physics ensemble study is the first effort to comprehensively evaluate the applicability of the WRF model to intense precipitation simulations over the HRB. Therefore, this study potentially contributes to the improvement of climate simulations for central China. • Cumulus schemes have primary impact on intense precipitation simulation while microphysics schemes have secondary impact. • A suitable WRF physical configuration is the combinations of the KF and WSM6 schemes. • This study potentially contributes to the improvement of climate simulations for central China. [ABSTRACT FROM AUTHOR]