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A 1000 year reconstruction of cool-season (November-April) precipitation was developed for each climate division in Arizona and New Mexico from a network of 19 tree-ring chronologies in the southwestern USA. Linear regression (LR) and artificial neural network (NN) models were used to identify the cool-season precipitation signal in tree rings. Using 1931-88 records, the stepwise LR model was cross-validated with a leave-one-out procedure and the NN was validated with a bootstrap technique. The final models were also independently validated using the 1896-1930 precipitation data. In most of the climate divisions, both techniques can successfully reconstruct dry and normal years, and the NN seems to capture large precipitation events and more variability better than the LR. In the 1000 year reconstructions the NN also produces more distinctive wet events and more variability, whereas the LR produces more distinctive dry events. The 1000 year reconstructed precipitation from the two models shows several sustained dry and wet periods comparable to the 1950s drought (e.g. 16th century mega drought) and to the post-1976 wet period (e.g. 1330s, 1610s). The impact of extreme periods on the environment may be stronger during sudden reversals from dry to wet, which were not uncommon throughout the millennium, such as the 1610s wet interval that followed the 16th century mega drought. The instrumental records suggest that strong dry to wet precipitation reversals in the past 1000 years might be linked to strong shifts from cold to warm El Niño-southern oscillation events and from a negative to positive Pacific decadal oscillation.

Chinese pine (Pinus tabulaeformis Carr.) trees from the Helan Mountain range in central China have been used to reconstruct total January–July precipitation from AD 1775 to 1998. For the calibration period R2adj = 0.52. Narrow rings are associated with below-average precipitation from March through August. Wide rings are produced in years when the East Asian summer monsoon front arrives early. We use local historical writings over the last 300 years about extreme climatic conditions between spring and early summer to verify the extreme years. Most of the extreme dry years could be identified in local historical documents. Another East Asian summer monsoon front related precipitation reconstruction from northern Helan Mountain is also used to verify this reconstruction. They are well correlated from year to year, with a correlation coefficient of 0.52 (N = 218), and the wet or dry extreme events are well matched in many cases. This comparison could indicate a spatial and temporal connection of spring to early summer climatic conditions for the southern to northern portion of the Helan Mountain region. The sustained wet period before the 20th century lasts from the 1850s to the 1890s, and the longest dry period before the 20th century is in the 1830s and 1840s, largely coinciding with a spring–summer drought in Kashmir. Overall, multiyear fluctuations, such as the spectacular large-scale drought of the late 1920s and droughts in the 1830s–1840s and the 1970s, are well captured in this reconstruction, but only the 1970s drought is in the instrumental period. The reconstruction shows increasing variance from the 18th to the late 20th century.

In order to link the monthly areal precipitation to large‐scale circulation patterns, a fuzzy indexing technique is used in conjunction with a fuzzy rule‐based technique and also a standard linear regression. After clustering the lag‐correlation centers, fuzziness is introduced, and several representative indices of the monthly areal precipitation in Arizona are calculated and interpreted. The relation between the indices and the precipitation is analyzed to develop the fuzzy model and then a multivariate linear regression model. To measure the forecasting capability of the models, the data are divided into a calibration period (1947–79) and a validation period (1980–1988). A comparison of the results shows that the fuzzy rule‐based model performs better than the regression model and has potential for monthly precipitation forecasting. Moreover, an adaptive fuzzy rule‐based framework is described so that the model can be used under climate change.