A constrained stochastic weather generator (CSWG) for producing daily mean air temperature and precipitation based on annual mean air temperature and precipitation from tree-ring records is developed and tested in this paper. The principle for stochastically generating daily mean air temperature assumes that temperatures in any year can be approximated by a sinusoidal wave function plus a perturbation from the baseline. The CSWG for stochastically producing daily precipitation is based on three additional assumptions: (1) In each month, the total precipitation can be estimated from annual precipitation if there exists a relationship between the annual and monthly precipitations. If that relationship exists, then (2) for each month, the number of dry days and the maximum daily precipitation can be estimated from the total precipitation in that month. Finally, (3) in each month, there exists a probability distribution of daily precipitation amount for each wet day. These assumptions allow the development of a weather generator that constrains statistically relevant daily temperature and precipitation predictions based on a specified annual value, and thus this study presents a unique method that can be used to explore historic (e.g., archeological questions) or future (e.g., climate change) daily weather conditions based upon specified annual values.

中文翻译：

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每日平均气温和降水的受限随机天气发生器

本文开发并测试了一种基于树轮记录的年平均气温和降水来产生日平均气温和降水的约束随机天气发生器（CSWG）。随机生成日平均气温的原理是假设任何一年的温度都可以通过正弦波函数加上基线的摄动来近似。随机产生日降水量的CSWG基于三个附加假设：（1）如果年降水量与月降水量之间存在关系，则每个月的总降水量可以从年降水量中估算出来。如果存在这种关系，则每个月（2），可以从该月的总降水量中估算出干旱天数和每日最大降水量。最后，（3）在每个月中，每个雨天都有每日降水量的概率分布。这些假设允许开发一种天气发生器，该气象发生器根据指定的年值限制统计上相关的每日温度和降水量预测，因此，本研究提出了一种独特的方法，可用于探索历史（例如考古问题）或未来（例如（气候变化）根据指定的年度值得出的每日天气状况。