Specific airborne fungal spore types trigger respiratory allergy symptoms in sensitive individuals. Aiming to reduce the risk for allergic patients, we constructed predictive models for the fungal spore circulation in Thessaloniki, Greece. Monthly and daily autoregressive forecasting models were developed (Dynamic Regression) for the airborne spore concentrations of Alternaria and Cladosporium, the most abundant fungal taxa in the area. The forecast horizons were respectively 12 months and one week. The accuracy of each predictive model was tested by means of six statistical criteria. Special attention was paid to the lag effect of all factors, both meteorological and fungal spore records. Aerobiological sampling was conducted over 1996–2002, using a Burkard trap. Records of 18 meteorological parameters were used for the same period. Residual analyses tested the adequacy of the models. Monthly forecasting models were highly significant, with adjusted R² = 0.68 for Alternaria, and 0.81 for Cladosporium. The respective values of adjusted R² for the daily models were 0.62 and 0.70. Cladosporium spore counts were consistently influenced by solar radiation, whereas Alternaria was influenced by air temperature (mean and minimum). For monthly forecasts, records of the preceding month, and the 12 month spore record was significant for Alternaria, whereas for Cladosporium, the lags were 12 and 24 months. With the daily models, the respective required periods were mainly the preceding one to two weeks and the last two years for both fungal taxa, in addition to a one‐year lag in the case of Cladosporium. Daily models could be further improved by co‐estimating interdiurnal variability and fungal spore sources.