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ABSTRACT Climate is a primary driver for extreme rainfall and flood events. In this paper, the temporally changing flood risk associated with the annual maximum 30-day rainfall (30-day AMR) and the annual maximum daily inflow ( AMDI) related to the Xidayang Reservoir catchment is analysed from a climatic context. This is the largest catchment in Daqing River Basin in North China and is highly prone to floods related to the East Asian summer monsoon. Two climate factors, the average May-June-July sea surface temperature anomalies in areas of the northern Indian Ocean and western Pacific Ocean, are identified. They show a high negative correlation with the AMDI and 30-day AMR. Bayesian nonstationary models are then developed for the AMDI and 30-day AMR using the climate predictors identified as covariates. We compared three types of models of AMDI and 30-day AMR: (1) time-invariant, (2) linear temporal trend and (3) climate informed, and found that the climate-informed models exhibit the best performance according to Deviance Information Criterion (DIC) and 90th percentile Bayesian coverage rate for both AMDI and 30-day AMR. A significant decreasing trend is identified in the AMDI and the 30-day AMR, which is found to be associated with the climate predictors. Leave one out cross validation (LOOCV) is used to demonstrate that these models have decent skill in predicting year-to-year variability in flood risk. This can help to provide flood/rainfall dynamic management measures for reservoirs in Daqing River Basin using information from before the beginning of monsoon season, thus facilitating adaptation to a changing climate. [ABSTRACT FROM AUTHOR]

We present the first global precipitation predictability estimates corresponding to the recently discovered flavors of El Niño Southern Oscillation (ENSO) that are encoded in the hidden states of Tropical Pacific sea surface temperatures identified using a non‐homogeneous hidden Markov model. For each calendar month and for each hidden state, we assess future precipitation predictability through the conditional standardized anomaly of the average and the standard deviation of monthly precipitation, at 1, 3, 6, 9, and 12 months lead times. We find statistically significant potential predictive skill for key regions for each hidden state and calendar month, even for 12‐months in the future. We apply the algorithm sequentially over the period of record to identify regions that can be consistently predicted for different lead times and calendar months. The cross‐validated correlation skill is demonstrably superior to that of regression with an ENSO index used in the same way. Plain Language Summary: El Niño Southern Oscillation (ENSO) information is routinely used by hydrologists, agriculturalists and others as a useful prognosticator of upcoming precipitation. Since the characterization of ENSO has recently been broadened to consider different "flavors," the NINO3.4 correlations used by many people may not be the most informative. Our machine learning model identified five hidden states of the tropical Pacific sea surface temperatures that correspond to the different ENSO flavors. We explore how future monthly precipitation may be predictable knowing which hidden state is currently identified and show that this leads to a higher predictability for up to almost a year in the future for many more calendar months in more regions of the world than NINO3.4 correlations. Key Points: Predictability of monthly global rainfall using hidden states of Sea Surface Temperatures in the tropical Pacific Ocean is demonstratedHighly significant skill for many global regions and months is identified by sampling of means and deviations given hidden stateA sequential application of the method leads to much higher cross‐validated correlations than those based on regression on an El Niño Southern Oscillation index [ABSTRACT FROM AUTHOR]

The article presents an analysis and interpretation of global sea surface temperature (SST) data using independent component analysis (ICA) and principal component analysis (PCA). It states that PCA represents multivariates and expands the variance explicated by components. It compares the capacity of PCA, the Varimax rotation and ICA in demonstrating climate unpredictability in SST anomaly (SSTA) data distributed worldwide.