During the last decade, rainfall monitoring using signal-level data from commercial microwave links (CMLs) in cellular communication networks has been proposed as a complementary way to estimate rainfall for large areas. Path-Averaged rainfall is retrieved between the transmitting and receiving cellular antennas of a CML. One rainfall estimation algorithm for CMLs is RAINLINK, which has been employed in different regions (e.g., Brazil, Italy, the Netherlands, and Pakistan) with satisfactory results. However, the RAINLINK parameters have been calibrated for a unique optimum solution, which is inconsistent with the fact that multiple similar or equivalent solutions may exist due to uncertainties in algorithm structure, input data, and parameters. Here, we show how CML rainfall estimates can be improved by calibrating all parameters of the algorithm systematically and simultaneously with the stochastic particle swarm optimization method, which is used for the numerical maximization of the objective function. An open dataset of approximately 2800 sub-links of minimum and maximum received signal levels over 15gmin intervals covering the Netherlands (g1/4g35g500gkm2) is employed: 12gd are used for calibration and 3 months for validation. A gauge-Adjusted radar rainfall dataset is utilized as a reference. Verification of path-Average daily rainfall shows a reasonable improvement for the stochastically calibrated parameters with respect to RAINLINK's default parameter settings. Results further improve when averaged over the Netherlands. Moreover, the method provides a better underpinning of the chosen parameter values and is therefore of general interest for calibration of RAINLINK's parameters for other climates and cellular communication networks.