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Highlights • Anomaly correlation exhibits persistent patterns at the global scale. • Anomaly correlation tends to be similarly high, or low, across initialization times. • Anomaly correlation tends to improve with initialization time. Abstract Global climate models (GCMs) produce informative seasonal forecasts of global precipitation months ahead of the occurrence for hydrological forecasting. Meanwhile, the skill of GCM forecasts varies by location and initialization time. In this paper, we investigate the anomaly correlation, which indicates the correspondence between forecasts and observations, for 10 sets of global precipitation forecasts in the North American Multi-Model Ensemble (NMME) project. We propose to use principal component analysis to characterize the variation of anomaly correlation. We identify the existence of spatial and temporal patterns at the global scale. The spatial pattern reveals that high (low) anomaly correlation at one initialization time coincides with high (low) anomaly correlation at other initialization times. In other words, for a grid cell, the anomaly correlation at different initialization times tends to be similarly high, or low. It is observed that some of the regions where grid cells are with overall high anomaly correlation tend to exhibit tele-connections with global climate drivers. On the other hand, the temporal pattern suggests that the anomaly correlation tends to improve with initialization time. This pattern is attributable to data assimilation that bases forecasts at a later initialization time on more global observations and simulations. Generally, the two patterns are effective and explain 50% to 70% of the variation of anomaly correlation for the 10 sets of NMME forecasts. The projections of anomaly correlation vectors onto the two patterns help illustrate where and when the NMME precipitation forecasts are skillful. [ABSTRACT FROM AUTHOR]

Abstract: The skill of global climate model (GCM) forecasts is usually indicated by the anomaly correlation between ensemble mean and observation. For GCM forecasts, anomaly correlation does not steadily improve with decreasing lead time but oscillates instead. This paper aims to address the oscillation and illustrate the relationship between anomaly correlation and lead time. We formulate the anomaly correlation of forecasts at different initialization times as a vector and pool anomaly correlation vectors across grid cells in the analysis. We propose two patterns to characterize the spatial and temporal variation of anomaly correlation in the three‐dimensional space of latitude, longitude, and initialization time. The first pattern suggests that the anomaly correlation at different initialization times is at a similar level. The second pattern indicates that the anomaly correlation linearly increases with decreasing lead time. These two patterns are tested using the eigenvectors through principal component analysis. They are first illustrated using the GFDL‐CM2p1‐aer04 forecasts of summer precipitation in China. They are further verified by another nine sets of North‐American Multi‐Model Ensemble (NMME) forecasts. Overall, the first pattern explains more variation than the second pattern. In total, the two patterns explain 42% of the variation of anomaly correlation for CanCM3, 59% for CanCM4, 42% for COLA‐RSMAS‐CCSM3), 45% for COLA‐RSMAS‐CCSM4, 59% for GFDL‐CM2p1, 67% for GFDL‐CM2p1‐aer04, 65% for GFDL‐CM2p5‐FLOR‐A06, 57% for GFDL‐CM2p5‐FLOR‐B01, 48% for NCAR‐CESM1, and 60% for NCEP‐CFSv2. The percentage of explained variation demonstrates the effectiveness of the two patterns as exploratory tools to analyze the predictive performance of GCM forecasts. [ABSTRACT FROM AUTHOR]

Global climate model (GCM) forecasts are an integral part of long-range hydroclimatic forecasting. We propose to use clustering to explore anomaly correlation, which indicates the performance of raw GCM forecasts, in the three-dimensional space of latitude, longitude, and initialization time. Focusing on a certain period of the year, correlations for forecasts initialized at different preceding periods form a vector. The vectors of anomaly correlation across different GCM grid cells are clustered to reveal how GCM forecasts perform as time progresses. Through the case study of Climate Forecast System Version 2 (CFSv2) forecasts of summer precipitation in China, we observe that the correlation at a certain cell oscillates with lead time and can become negative. The use of clustering reveals two meaningful patterns that characterize the relationship between anomaly correlation and lead time. For some grid cells in Central and Southwest China, CFSv2 forecasts exhibit positive correlations with observations and they tend to improve as time progresses. This result suggests that CFSv2 forecasts tend to capture the summer precipitation induced by the East Asian monsoon and the South Asian monsoon. It also indicates that CFSv2 forecasts can potentially be applied to improving hydrological forecasts in these regions. For some other cells, the correlations are generally close to zero at different lead times. This outcome implies that CFSv2 forecasts still have plenty of room for further improvement. The robustness of the patterns has been tested using both hierarchical clustering and k-means clustering and examined with the Silhouette score. [ABSTRACT FROM AUTHOR]

Under climate change, droughts are causing increasingly larger damages around the world, attracting the attention of governments and researchers. In this study, the terrestrial water storage anomalies (TWSA) obtained from a Gravity Recovery and Climate Experiment (GRACE) were coupled with precipitation, creating a Combined Climatological Deviation Index (CCDI). This index was used to analyze the drought events that occurred in the major basins of Central Asia between April 2002–June 2017. The main conclusions are as follows: (1) the certain but not significant (i.e., p >.05) impacts of precipitation were detected on the total water storage anomalies (TWSA) of three basins (i.e., Irtysh River Basin (IRB), Syr Darya Basin (SDB), and Amu Darya Basin (ADB)) that originate from the Tienshan Mountains based on the correlation coefficient between precipitation and terrestrial water storage (TWS); (2) the Drought Severity Index (DSI), the Palmer Drought Severity Index (PDSI), and the Standardized Precipitation Index over a 12-month scale (SPI12) were related to the CCDI in the major basins of Central Asia, suggesting that the CCDI can be applied effectively to assess the drought characteristics of these basins; (3) the most severe drought events detected by the CCDI in the IRB, SDB, and ADB occurred in 2012, 2014, and 2014, respectively. A humidification trend was observed for the IRB, while a drought trend was observed in the SDB and ADB. Therefore, it appears that the CCDI can be effectively applied to determine the severity of droughts, increase water-saving awareness, and avoid the effects of droughts. • Precipitation has little effect on TWS in Central Asia. • CCDI can character the drought events in Central Asia well. • The drought severity from CCDI was more severe than that from other traditional drought indices. • All the drought indices were discovered the obvious drought events in Central Asia during 2008–2009. [ABSTRACT FROM AUTHOR]

Based on the precipitation data from 96 weather stations in Northwest China (NWC) during 1960–2013, the Continuous Wavelet Transform (CWT) and the Mann-Kendall (MK) test were applied to analyze the precipitation spatiotemporal variations at different time scales. The relationships between the original precipitation and different periodic components were investigated. The results indicated that the annual precipitation was significantly increasing (P < 0.01) at the rate of 0.55 mm/a in the NWC. In terms of seasonal precipitation, the summer original precipitation significantly increased (P < 0.05) in the Southern Altay Mountain Basin (SAMB), Qaidam Basin (QB), Qiang Tang Plateau Basin (QTPB), Turpan-Hami Basin (THB), Tarim Desert Basin (TDB), Northern Tianshan Mountain Basin (NTMB) and NWC. For the winter original precipitation, except the Inner Mongolia Inland Rivers Basin and Northern Kunlun Mountain Basin, the significant increases (P < 0.05) were detected in the other sub-basins. In terms of monthly precipitation, significant increases were detected in January in the SAMB, NTMB and NWC, and July in the QB, Headstreams of Tarim River Basin (HTRB) and N. Additionally, most of the increasing and decreasing trends began in the mid-1980s or mid-1990s. Moreover, the periodic components were not always similar to the original data with the significant trends. The dominant scale of the original data from the periodic components was different in spatiotemporal distribution. Meanwhile, the relationship between the precipitation and El Niño-Southern Oscillation (ENSO) was different from period to period and from time scale to time scale. This study will help to develop better management measures to account for climate change and the supply/demand of water. [ABSTRACT FROM AUTHOR]

• A Gamma-Gaussian model is built upon quantile mapping to calibrate monthly and seasonal GCM precipitation forecasts. • Quantile mapping is shown to be effective in correcting bias but fail to ensure forecast reliability and coherence. • The Gamma-Gaussian model can effectively generate unbiased, reliable, and coherent ensemble precipitation forecasts. Calibration is necessary for improving raw forecasts generated by global climate models (GCMs) to fully utilize potential benefits of the forecasts in practical applications. Based on quantile mapping (QM), this paper proposes a five-parameter Gamma-Gaussian model to calibrate monthly and seasonal GCM precipitation forecasts. While QM directly maps forecasts to observations without accounting for the dependency relationship, the Gamma-Gaussian model employs the Gamma distribution to normalize precipitation forecasts and observations using normal quantile transform (NQT) and then formulates a bivariate Gaussian distribution to characterize the dependency relationship. A case study is devised to calibrate global precipitation forecasts generated by the Climate Forecast System version 2 (CFSv2) using both QM and Gamma-Gaussian models. The results show that both QM and Gamma-Gaussian models can effectively correct biases. While QM can improve forecast reliability to some degree by reducing biases, reliability is not always satisfactory. The Gamma-Gaussian model outperforms QM because it can ensure forecast reliability and coherence. To facilitate the selection of marginal distributions for the purpose of calibrating GCM precipitation forecasts, six alternative distributions, i.e., Gamma, lognormal, generalized extreme value, generalized logistic, Pearson type III and Kappa distributions, are employed to characterize the marginal distribution of forecasts (observations) in NQT. It is observed that the Gamma distribution is overall the most suitable and that the alternative distributions tend to fit sample-specific noises and get penalized under cross validation. Overall, the Gamma-Gaussian model can serve as an effective tool to calibrate raw GCM forecasts for hydrological modeling and water resources management. [ABSTRACT FROM AUTHOR]