Streamflow forecasting in drylands is challenging. Data are scarce, catchments are highly human-modified and streamflow exhibits strong nonlinear responses to rainfall. The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area. We adopted four different lead times: one month ahead for monthly scale and two, three and four months ahead for seasonal scale. The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling. Then, time series model techniques were applied, i.e., the locally constant, the locally averaged, the k-nearest-neighbours algorithm (k-NN) and the autoregressive (AR) model. The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology. Our approach outperformed the streamflow climatology for all monthly streamflows. On average, the former was 25％ better than the latter. The seasonal streamflow forecasting (SSF) was also reliable (on average, 20％ better than the climatology), failing slightly only for the high flow season of one catchment (6％ worse than the climatology). Considering an uncertainty envelope (probabilistic forecasting), which was considerably narrower than the data standard deviation, the streamflow forecasting performance increased by about 50％ at both scales. The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale, while they were by the forecast lead time at seasonal scale. The best-fit and worst-fit time series model were the k-NN approach and the AR model, respectively. The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35％ of data gaps. The developed data-driven approach is mathematical and computationally very simple, demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds. Our findings may be part of drought forecasting systems and potentially help allocating water months in advance. Moreover, the developed strategy can serve as a baseline for more complex streamflow forecast systems.