Machine-learning weather models now rival or surpass operational numerical weather prediction (NWP), yet no single model dominates across variables, pressure levels, lead times, or regions, and the returns from ever-larger individual models are diminishing. We present TaiJi (after the yin-yang concept of complementary opposites), a spatiotemporal adaptive ensemble framework that recasts optimal model-weight combination as a learned prediction problem: a lightweight convolutional neural network, conditioned on the recent atmospheric state, predicts at every grid point and lead time affine combination weights and an additive residual for the constituent forecasts. The combiner is trained end-to-end over Pangu-Weather, GraphCast, FuXi, IFS-HRES, and the IFS-ENS ensemble mean, a hybrid set of ML and NWP constituents, at less than a tenth of the cost of training any of its ML constituents. Under the WeatherBench 2 protocol (2020 test year), TaiJi outperforms the strongest baseline in nearly every combination of the eight core variables, lead times up to 10 days and two skill metrics (RMSE and ACC), and this advantage extends to heavy-precipitation extremes. The same state-conditional principle transfers to tropical-cyclone track forecasting: by fusing constituent tracks rather than gridded fields, TaiJi-TC reduces track error relative to the equally weighted multi-model consensus at lead times up to 96 h, by about 10% at 36 h, halves the slow along-track bias shared by all constituents up to 60 h, and lowers landfall position error by 13-15% at 60-84 h. The learned weights vary coherently with latitude, season and lead time rather than following a fixed rule. TaiJi thus offers an inexpensive route to better global forecasts that builds on, rather than replaces, existing forecast models.

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