We developed a data-driven prediction system for precipitation (Pr) and near-surface temperature (T2m) over the global domain by utilizing NASA’s satellite observations and the associated reanalysis products, with the focus on S2S hydrologic prediction. Our approach is based on a well-established methodology of linear inverse modeling modified and adapted by our science team for high-resolution modeling of precipitation. The key element of this new methodology is the usage of a so-called pseudo-precipitation (PP) variable, equal to the actual Pr in the case the precipitation is occurring and, otherwise, equal to the (negative) air-column integrated water-vapor saturation deficit (the amount of water vapor to be added to the air column to achieve saturation at each vertical level). The model Pr and T2m forecasts are then validated against the observed fields as usual. It is in part through the T2m modeling that the S2S predictability associated with low-frequency dynamical climate modes would filter in the potential S2S precipitation prediction skill within the proposed framework. The system above was shown to be an efficient tool for simulating independent sequencies of daily evolution of the global T2m and Pr fields with spatiotemporal characteristics strikingly similar to the observed characteristics. We are currently evaluating its predictive capabilities. We hypothesize that: (i) the resulting data-driven prediction system may rival the S2S skill of state-of-the-art NWP models in predicting extreme events in widely diverse precipitation environments; and that (ii) it will do so due to its ability to capture tropical low-frequency modes, as well as tropical-to-extratropical teleconnections in the context of a globally connected, seamless multi-scale model. Our goals are to demonstrate both the proposed model skill and its internal dynamical sources, as well as to map out the ensuing implications of these results for hydrologic predictions throughout the globe.

Reliability of future global warming projections depends on how well climate models reproduce the observed climate change over the twentieth century. In this regard, deviations of the model simulated climate change from observations, such as a recent “pause” in global warming, have received considerable attention. Such decadal mismatches between model simulated and observed climate trends are common throughout the twentieth century, and their causes are still poorly understood. Here we show that the discrepancies between the observed and simulated climate variability on decadal and longer time scale have a coherent structure suggestive of a pronounced global multidecadal oscillation. Surface temperature anomalies associated with this variability originate in the North Atlantic and spread out to the Pacific and Southern oceans and Antarctica, with Arctic following suit in about 25–35 years. While climate models exhibit various levels of decadal climate variability and some regional similarities to observations, noneof the model simulations considered match the observed signal in terms of its magnitude, spatial patterns and their sequential time development. These results highlight a substantial degree of uncertainty in our interpretation of the observed climate change using current generation of climate models.