Streamflow disaggregation is used to preserve statistical attributes of time series across multiple sites and timescales. Several algorithms for spatial disaggregation and for disaggregation of annual to monthly flows are available. However, the disaggregation of monthly to daily or weekly to daily flows remains a challenge. A new algorithm is presented for simultaneously disaggregating monthly flows at a number of sites and daily flows at an index site to daily flows at a number of sites on a drainage network. The continuity of flow in time across months at each site as well as the intersite flow pattern are preserved. The disaggregated daily flows at the multiple sites are conditioned on the spatial (across site) pattern of monthly flows at the respective sites. The probability distribution of the vector of disaggregated flows conditional on the multisite monthly flows is approximated nonparametrically using the k nearest neighbors of the monthly spatial flow pattern. A constrained optimization problem is solved to adaptively estimate the disaggregated flows in space and time for each such neighborhood. An application to data from a tributary of the Colorado River is used to illustrate the modeling process. ¿ 2000 American Geophysical Union