This study investigates localized objective forecasting algorithms for intelligent grid systems aimed at improving the accuracy of temperature and precipitation forecasts from numerical models in the Qianxinan region of Guizhou Province. We employed two multi-model ensemble methods—bias correction ensemble averaging and weighted bias correction—utilizing dynamic error weighting, separate modeling for high and low temperatures, and precipitation classification statistics. Corrections were applied to temperature and precipitation forecasts from numerical models (ECMWF, GERMANY, and JAPAN) at both station and grid levels. A comparative analysis of forecast errors, accuracy, and skill was conducted before and after the corrections using various evaluation metrics.The results indicate that (1) the bias correction forecasts significantly reduced numerical model forecast errors, with 2 m temperature and 12-hour accumulated precipitation errors decreasing to below 2.5 °C and 2.5 mm, respectively; (2) the weighted bias correction based on dynamic errors notably enhanced the accuracy of temperature and precipitation forecasts. The separate modeling for high and low temperatures, along with precipitation classification, further improved forecasting performance. Specifically, the accuracy of 2 m temperature forecasts increased by 41.48% and 12.47% at grid and station levels, respectively, compared to the best-performing model, ECMWF. Additionally, the accuracy of light rain forecasts at stations improved by 23.9% compared to the top model, JAPAN. Moreover, the accuracy for minimum temperature and light rain forecasts exceeded local forecasters" historical records, with improvements of 1.44% and 27.8%, respectively.The multi-model ensemble bias correction forecasting model, utilizing separate modeling for high and low temperatures and graded precipitation statistics, effectively reduced forecast errors for 2 m temperature and 12-hour accumulated precipitation, significantly enhancing forecast accuracy. Notably, the improvements in 2 m temperature grid forecasts and precipitation station corrections demonstrate considerable effectiveness, with light rain forecasts outperforming historical predictions from local forecasters, providing substantial reference value for operational forecasting.