The complexity of the human-environment interface predicates the need for tools and techniques that can enable the effective translation of weather and climate products into decision-relevant information. Indices are a category of such tools that may be used to simplify multi-faceted climate information for economic and other decision-making. Climate indices for tourism have been popularized in the literature over the past three decades, but despite their prevalence, these indices have a number of limitations, including coarse temporal resolution, subjective rating and weighting schemes, and lack of empirical validation. This paper critically assesses the design of the tourism climate index, the holiday climate index-beach, and a new, mathematically optimized index developed for the unique contextual realities of Great Lakes beach tourism. This new methodology combines the use of expert knowledge, stated visitor preferences, and mathematical optimization to develop an index that assigns daily weather scores based on four weather sub-indices (thermal comfort, wind speed, precipitation, and cloud cover). These daily scores are then averaged to the monthly level and correlated to visitation data at two beach parks in Ontario (Canada). This optimized index demonstrates a strong fit (R = 0.734, 0.657) with observed visitation at Pinery Provincial Park and Sandbanks Provincial Park, outperforming both the tourism climate index (R = 0.474, 0.018) and the holiday climate index-beach (R = 0.668, 0.427). This study advances our understanding of the magnitude and seasonality of weather impact on beach tourist visitation and can inform decision-making of tourism marketers and destination managers.