A model to generate stochastic streamflow projections conditioned on quasi‐oscillatory climate indices such as Pacific Decadal Oscillation (PDO) and Atlantic Multi‐decadal Oscillation (AMO) is presented. Recognizing that each climate index has underlying band‐limited components that contribute most of the energy of the signals, we first pursue a wavelet decomposition of the signals to identify and reconstruct these features from annually resolved historical data and proxy based paleoreconstructions of each climate index covering the period from 1650 to 2012. A K‐Nearest Neighbor block bootstrap approach is then developed to simulate the total signal of each of these climate index series while preserving its time‐frequency structure and marginal distributions. Finally, given the simulated climate signal time series, a K‐Nearest Neighbor bootstrap is used to simulate annual streamflow series conditional on the joint state space defined by the simulated climate index for each year. We demonstrate this method by applying it to simulation of streamflow at Lees Ferry gauge on the Colorado River using indices of two large scale climate forcings: Pacific Decadal Oscillation (PDO) and Atlantic Multi‐decadal Oscillation (AMO), which are known to modulate the Colorado River Basin (CRB) hydrology at multidecadal time scales. Skill in stochastic simulation of multidecadal projections of flow using this approach is demonstrated.

We propose a time series modeling approach based on nonlinear dynamical systems to recover the underlying dynamics and predictability of streamflow and to produce projections with identifiable skill. First, a wavelet spectral analysis is performed on the time series to identify the dominant quasiperiodic bands. The time series is then reconstructed across these bands and summed to obtain a signal time series. This signal is embedded in a D‐dimensional space with an appropriate lag τ to reconstruct the phase space in which the dynamics unfolds. Time‐varying predictability is assessed by quantifying the divergence of trajectories in the phase space with time, using Local Lyapunov Exponents. Ensembles of projections from a current time are generated by block resampling trajectories of desired projection length, from the K‐nearest neighbors of the current vector in the phase space. This modeling approach was applied to the naturalized historical and paleoreconstructed streamflow at Lees Ferry gauge on the Colorado River, which offered three interesting insights. (i) The flows exhibited significant epochal variations in predictability. (ii) The predictability of the flow quantified by Local Lyapunov Exponent is related to the variance of the flow signal and selected climate indices. (iii) Blind projections of flow during epochs identified as highly predictable showed good skill in capturing the distributional and threshold exceedance statistics and poor performance during low predictability epochs. The ability to assess the potential skill of these long lead projections opens opportunities to perceive hydrologic predictability and consequently water management in a new paradigm.

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