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Amid climate change, extreme droughts have occurred more frequently, accompanied by increasing socio‐economic‐environmental losses. To be prepared for upcoming droughts, proactive drought management planning based on drought forecasting is being highlighted. This study, therefore, developed a long‐term drought forecasting model using Burg entropy spectral analysis with the maximal overlap discrete wavelet transform across the continental United States (CONUS). The model forecasted monthly self‐calibrated Palmer Drought Severity Index (scPDSI) up to median lead times of 12 months with root mean square error less than 0.8 and the modified Nash‐Sutcliffe efficiency coefficient greater than 0.6 across CONUS. The lead‐time with an acceptable accuracy was affected by how the model parameterized low‐frequency structure of scPDSI that was significantly correlated to sea surface temperature climate mode. The correlation between low‐frequency signals and Niño‐3.4 tended to be weaker for the regions located at higher elevations. Thus, higher variability in spectral structures of low‐frequency signals at higher elevation‐regions led to shorter memory/persistency of time series that shortened the forecast lead time. Besides, higher variability in precipitation linked to the variability of scPDIS also shortened the forecast lead time. To appraise the applicability of the suggested forecast model to drought management planning, drought characteristics (i.e., severity, duration, and area) were obtained by the three‐dimensional drought clustering algorithm for the forecasted and observed scPDSI. Then, risk was analyzed for drought characteristics using the vine copula. Risk analysis for drought characteristics forecasted up to a 12‐month lead time showed the potential for applicability of the model to drought mitigation plans. Key Points: Burg entropy accurately estimated the spectral densities of low‐frequency signals of scPDSI that are important for long‐term forecastingThe proposed model forecasted scPDSI with acceptable accuracies up to median lead times of 12 months across CONUSRisk analysis for drought characteristics forecasted up to 12 months ahead showed the proposed model would be useful for drought mitigation [ABSTRACT FROM AUTHOR]

Droughts and floods are extreme events that have tremendous socioeconomic impact. Many studies have investigated their possible variations under the effects of climate changes, while their intraseasonal‐to‐seasonal characteristics have received less attention. Based on observation‐constrained monthly soil moisture (SM) datasets since 1980, we analysed the spatiotemporal characteristics of global SM at the intraseasonal‐to‐seasonal time scales and the associated influencing factors. The results were then used to evaluate the performance of simulations by Coupled Model Intercomparison Project Phase 6 (CMIP6) models. It is found that the frequencies of extreme wet events are slightly higher than those of extreme dry events, but their spatial distributions are similar and are both affected by precipitation variability. Extreme events tend to have longer durations than nonextreme events, and their global mean durations are 4–5 months. Globally, dry events persist longer than wet events. Maximum SM anomalies are strongly affected by earlier precipitation and this effect is stronger in a transitional climate. The onset and peak times of SM anomalies in a year occur most frequently near the peak time of precipitation rather than that of SM. Approximately half of CMIP6 models overestimate the durations of both dry and wet events and approximately one third of models only underestimate wet events durations. Different versions of the same model produce similar event durations. The findings of this study enhance understanding of the intraseasonal‐to‐seasonal variabilities of global droughts and floods and could provide guidance for future study of land–atmosphere interactions and prediction of extreme events. [ABSTRACT FROM AUTHOR]