Subseasonal forecasts provide valuable guidance for decision-making but remain limited by systematic biases and coarse spatial resolution, particularly over regions with complex orography. Quantile mapping (QM) is widely used for post-processing because it enables effective bias correction and downscaling, yet it applies a distributional correction and therefore neglects the case-specific dependence between forecasts and observations. To move toward a more integrated approach, we explore a diffusion-based generative framework for simultaneous probabilistic bias correction and high-resolution downscaling, with lead time explicitly included as a conditioning variable. We employ a conditional denoising diffusion model (DM) trained on high-resolution gridded observations (2 km) and conditioned on coarse-resolution ensemble-mean subseasonal forecasts (36 km) from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) subseasonal system. We focus here on a univariate temperature model and on training strategies. By learning conditional distributions of observations given forecasts and lead time, diffusion-based generative models enable case-dependent corrections that exploit forecast–observation relationships. Two training configurations are examined to assess how lead-time conditioning and data availability influence the learning of biases. Results show that the DM recovers realistic fine-scale temperature patterns and achieves meaningful lead-time-dependent bias correction across subseasonal lead times, even when trained on relatively limited datasets. However, compared to QM, the diffusion-based forecasts exhibit lower skill in probabilistic metrics such as the continuous ranked probability score (CRPS) and discrimination for extremes. Overall, this work represents a first step toward lead-time-aware subseasonal post-processing using DMs and highlights directions for future methodological development.