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Global climate change is predicted to affect both the spatial and annual distributions of vector-borne diseases. Tick-borne diseases are particularly sensitive to the changing climatic conditions. Modelling them is, however, challenging due to the input-intensity of these models. A framework with low number of inputs (easily accessible weekly temperature data and week numbers) on modelling the seasonality of Lyme borreliosis incidences is presented. The modelling framework enables predicting the annual distribution of Ixodes ricinus tick's biting activity and Lyme borreliosis in two cascading phases, incorporating a population dynamics approach. The model is calibrated for Hungary as a case study, for the period of 1998-2008, using tick-borne encephalitis series as a proxy for biting activity. Prediction to the future period of 2081-2100 is also provided. Climate change may significantly alter both the annual distribution of I. ricinus activity and that of the Lyme borreliosis incidences. The currently unimodal annual distribution of Lyme borreliosis is predicted to become bimodal with a long summer pause and a spring maximum shifted 8 weeks earlier.