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The impacts of the interdecadal variability of the Pacific and the Atlantic Oceans on precipitation over the Central Andes during the austral summer (December-January-February, DJF) are investigated for the 1921–2010 period based on monthly gridded precipitation data and low-pass filtered time series of the Niño 4 index (IN4), the Niño 1 + 2 index with Niño 3.4 index removed (IN1+2 * ), Atlantic Multidecadal Oscillation (AMO), and Interdecadal Pacific Oscillation (IPO) indices, and the three first rotated principal components of the interdecadal component of the sea surface temperature (SST) anomalies over the Atlantic Ocean. A rotated empirical orthogonal function (REOF) analysis of precipitation in the Central Andes (10°S–30°S) yields two leading modes, RPC1 and RPC2, which represent 40.4% and 18.6% of the total variance, respectively. REOF1 features a precipitation dipole between the northern Bolivian and the Chilean Altiplano. REOF2 also features a precipitation dipole, with highest negative loading over the southern Peruvian Andes. The REOF1 positive phase is associated with moisture transport from the lowlands toward the Bolivian Altiplano, induced by upper-level easterly wind anomalies over the Central Andes. At the same time conditions tend to be dry over the southern Peruvian Andes. The positive phase of REOF2 is related to weakened moisture transport, induced by upper-level westerly wind anomalies over Peru. The IPO warm phase induces significant dry anomalies over the Bolivian Altiplano, albeit weaker than during the IN4 warm phase, via upper-level westerly wind anomalies over the Central Andes. No significant relationship was found between Central Andean precipitation and the AMO on interdecadal timescales. 

Time series forecasting with additional spatial information has attracted a tremendous amount of attention in recent research, due to its importance in various real-world applications on social studies, such as conflict prediction and pandemic forecasting. Conventional machine learning methods either consider temporal dependencies only, or treat spatial and temporal relations as two separate autoregressive models, namely, space-time autoregressive models. Such methods suffer when it comes to long-term forecasting or predictions for large-scale areas, due to the high nonlinearity and complexity of spatio-temporal data. In this paper, we propose to address these challenges using spatio-temporal graph neural networks. Empirical results on Violence Early Warning System (ViEWS) dataset and U.S. Covid-19 dataset indicate that our method significantly improved performance over the baseline approaches. 

Abstract Precipitation is one of the most difficult variables to estimate using large-scale predictors. Over South America (SA), this task is even more challenging, given the complex topography of the Andes. Empirical–statistical downscaling (ESD) models can be used for this purpose, but such models, applicable for all of SA, have not yet been developed. To address this issue, we construct an ESD model using multiple-linear-regression techniques for the period 1982–2016 that is based on large-scale circulation indices representing tropical Pacific Ocean, Atlantic Ocean, and South American climate variability, to estimate austral summer [December–February (DJF)] precipitation over SA. Statistical analyses show that the ESD model can reproduce observed precipitation anomalies over the tropical Andes (Ecuador, Colombia, Peru, and Bolivia), the eastern equatorial Amazon basin, and the central part of the western Argentinian Andes. On a smaller scale, the ESD model also shows good results over the Western Cordillera of the Peruvian Andes. The ESD model reproduces anomalously dry conditions over the eastern equatorial Amazon and the wet conditions over southeastern South America (SESA) during the three extreme El Niños: 1982/83, 1997/98, and 2015/16. However, it overestimates the observed intensities over SESA. For the central Peruvian Andes as a case study, results further show that the ESD model can correctly reproduce DJF precipitation anomalies over the entire Mantaro basin during the three extreme El Niño episodes. Moreover, multiple experiments with varying predictor combinations of the ESD model corroborate the hypothesis that the interaction between the South Atlantic convergence zone and the equatorial Atlantic Ocean provoked the Amazon drought in 2015/16.