Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Extreme events, such as drought, heat wave, cold wave, flood, and extreme rainfall, have received increasing attention in recent decades due to their wide impacts on society and ecosystems. Meanwhile, the compound extremes (i.e., the simultaneous or sequential occurrence of multiple extremes at single or multiple locations) may exert even larger impacts on society or the environment. Thus, the past decade has witnessed an increasing interest in compound extremes. In this study, we review different approaches for the statistical characterization and modeling of compound extremes in hydroclimatology, including the empirical approach, multivariate distribution, the indicator approach, quantile regression, and the Markov Chain model. The limitation in the data availability to represent extremes and lack of flexibility in modeling asymmetric/tail dependences of multiple variables/events are among the challenges in the statistical characterization and modeling of compound extremes. Major future research endeavors include probing compound extremes through both observations with improved data availability (and statistical model development) and model simulations with improved representation of the physical processes to mitigate the impacts of compound extremes.

In past decades, severe drought events have struck different regions around the world, leading to huge losses to a wide array of environmental and societal sectors. Because of wide impacts of drought, it is of critical importance to monitor drought in near–real time and provide early warning. This article provides an overview of the development of drought monitoring and prediction systems (DMAPS) at regional and global scales. After introducing drought indicators, drought monitoring (based on different data sources and tools) is summarized, along with an introduction of statistical and dynamical drought prediction approaches. The current progress of the development and implementation of DMAPS with various indicators at different temporal and/or spatial resolutions, based on the land surface modeling, remote sensing, and seasonal climate forecast, at the regional and global scales is then reviewed. Advances in drought monitoring with multiple data sources and tools and prediction from multimodel ensembles are highlighted. Also highlighted are challenges and opportunities, including near-real-time and long-term data products, indicator linkage to impacts, prediction skill improvement, and information dissemination/communication. The review of different components of these systems will provide useful guidelines and insights for the future development of effective DMAPS to aid drought modeling and management.