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This paper deals with the probabilistic short-range temperature forecasts over synoptic meteorological stations across Iran using non-homogeneous Gaussian regression (NGR). NGR creates a Gaussian forecast probability density function (PDF) from the ensemble output. The mean of the normal predictive PDF is a bias-corrected weighted average of the ensemble members and its variance is a linear function of the raw ensemble variance. The coefficients for the mean and variance are estimated by minimizing the continuous ranked probability score (CRPS) during a training period. CRPS is a scoring rule for distributional forecasts. In the paper of Gneiting et al. (Mon Weather Rev 133:1098–1118, 2005), Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is used to minimize the CRPS. Since BFGS is a conventional optimization method with its own limitations, we suggest using the particle swarm …