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<p>Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ET<span class="inline-formula">_o</span>) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ET<span class="inline-formula">_o</span> forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ET<span class="inline-formula">_o</span> forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ET<span class="inline-formula">_o</span> forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ET<span class="inline-formula">_o</span> forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ET<span class="inline-formula">_o</span> forecasts. To test this hypothesis, we calibrate raw seasonal ET<span class="inline-formula">_o</span> forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ET<span class="inline-formula">_o</span> forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (<span class="inline-formula"><i>r</i></span>) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ET<span class="inline-formula">_o</span> forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ET<span class="inline-formula">_o</span> forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ET<span class="inline-formula">_o</span> forecasting will benefit from correcting time-dependent errors through trend reconstruction.</p>