This research introduces a novel method for accurate and consistent hydrological forecasting at multiple timescales. Deep learning (DL) models are increasingly being used for hydrological forecasting across various timescales (hourly, daily, etc.). However, one of the main challenges with multi-timescale DL-based hydrological forecasting is the potential inconsistency (discrepancy) between forecasts across different timescales. Inconsistent multi-timescale forecasts can be problematic especially when decision-making relies on forecasts across different timescales. This paper introduces a hierarchical DL (HDL) model incorporating temporal hierarchical reconciliation (THR) with DL models for consistent multi-timescale streamflow forecasting. HDL is developed, deployed, and tested for multi-step (seven days ahead), multi-timescale (daily and weekly) streamflow forecasting using over 400 catchments across the contiguous United States. HDL is based on long short-term memory (LSTM) networks and implements THR through a differentiable output layer. HDL consistently improved the median Nash Sutcliffe efficiency (NSE) of daily streamflow forecasts (e.g., by 3.01% for lead time one) compared to a multi-timescale LSTM benchmark and resulted in significantly more accurate forecasts in more than 65% of the catchments at the weekly scale than daily forecast aggregation. HDL performance is influenced by both the THR formulation and the accuracy of the forecasts at different timescales. For instance, at the weekly timescale, HDL yielded a notable median improvement of 9.45% in NSE for the lowest-performing decile of catchments (NSE < 0.37), which are typically the most challenging to forecast. The proposed HDL framework offers a novel, generalizable, and promising approach for multi-timescale forecasting across diverse water resources forecasting tasks.