Local natural gas distribution companies rely on accurate forecasts of daily demand/flow for buying and delivering gas to their customers. Such forecasts are done by devising computational methods that take into account weather data and historical daily flow in regions of interest. However, in some cases, historical measured daily data is not available. In this work, multiparameter linear regression models are built when only monthly/billing-cycle flow data is available for disaggregation and to forecast daily flow. Results show monthly consumption data can be used in conjunction with daily weather data to provide accurate estimates of daily demand. To improve models, adjustments such as Weekday vs. Weekend, Day of Week, and Prior Day weather are incorporated into the models. In comparison to the base linear regression models, these adjustments can decrease the forecast error by up to 20% using the best combination of mentioned adjustments.